{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T11:45:51Z","timestamp":1774352751921,"version":"3.50.1"},"reference-count":175,"publisher":"Springer Science and Business Media LLC","issue":"S1","license":[{"start":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T00:00:00Z","timestamp":1689811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T00:00:00Z","timestamp":1689811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10462-023-10557-6","type":"journal-article","created":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T15:04:45Z","timestamp":1689865485000},"page":"1279-1335","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Tackling class imbalance in computer vision: a contemporary review"],"prefix":"10.1007","volume":"56","author":[{"given":"Manisha","family":"Saini","sequence":"first","affiliation":[]},{"given":"Seba","family":"Susan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,20]]},"reference":[{"key":"10557_CR1","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/978-981-15-0947-6_31","volume-title":"Embedded systems and artificial intelligence: proceedings of ESAI 2019, Fez, Morocco","author":"A Adadi","year":"2020","unstructured":"Adadi A, Berrada M (2020) Explainable AI for healthcare: from black box to interpretable models. Embedded systems and artificial intelligence: proceedings of ESAI 2019, Fez, Morocco. Springer Singapore, Singapore, pp 327\u2013337"},{"key":"10557_CR2","doi-asserted-by":"crossref","first-page":"115528","DOI":"10.1109\/ACCESS.2019.2932786","volume":"7","author":"S Afzal","year":"2019","unstructured":"Afzal S, Maqsood M, Nazir F, Khan U, Aadil F, Awan KM, Mehmood I, Song O-Y (2019) A data augmentation-based framework to handle class imbalance problem for Alzheimer\u2019s stage detection. IEEE Access 7:115528\u2013115539","journal-title":"IEEE Access"},{"issue":"5","key":"10557_CR3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/MSP.2015.116","volume":"13","author":"Z Akhtar","year":"2015","unstructured":"Akhtar Z, Micheloni C, Foresti GL (2015) Biometric liveness detection: challenges and research opportunities. IEEE Secur Privacy 13(5):63\u201372","journal-title":"IEEE Secur Privacy"},{"issue":"21","key":"10557_CR4","doi-asserted-by":"crossref","first-page":"8268","DOI":"10.3390\/s22218268","volume":"22","author":"SY Alaba","year":"2022","unstructured":"Alaba SY, Nabi MM, Shah C, Prior J, Campbell MD, Wallace F, Ball JE, Moorhead R (2022) Class-aware fish species recognition using deep learning for an imbalanced dataset. Sensors 22(21):8268","journal-title":"Sensors"},{"key":"10557_CR5","doi-asserted-by":"crossref","first-page":"201173","DOI":"10.1109\/ACCESS.2020.3033784","volume":"8","author":"TM Alam","year":"2020","unstructured":"Alam TM, Shaukat K, Hameed IA, Luo S, Sarwar MU, Shabbir S, Li J, Khushi M (2020) An investigation of credit card default prediction in the imbalanced datasets. IEEE Access 8:201173\u2013201198","journal-title":"IEEE Access"},{"issue":"9","key":"10557_CR6","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.3390\/diagnostics12092115","volume":"12","author":"TM Alam","year":"2022","unstructured":"Alam TM, Shaukat K, Khan WA, Hameed IA, Almuqren LA, Raza MA, Aslam M, Luo S (2022) An efficient deep learning-based skin cancer classifier for an imbalanced dataset. Diagnostics 12(9):2115","journal-title":"Diagnostics"},{"key":"10557_CR8","doi-asserted-by":"crossref","first-page":"20235","DOI":"10.1109\/ACCESS.2021.3054484","volume":"9","author":"HS Alghamdi","year":"2021","unstructured":"Alghamdi HS, Amoudi G, Elhag S, Saeedi K, Nasser J (2021) Deep learning approaches for detecting COVID-19 from chest X-ray images: a survey. Ieee Access 9:20235\u201320254","journal-title":"Ieee Access"},{"issue":"11","key":"10557_CR9","doi-asserted-by":"crossref","first-page":"4040","DOI":"10.3390\/s22114040","volume":"22","author":"A Alia","year":"2022","unstructured":"Alia A, Maree M, Chraibi M (2022) A hybrid deep learning and visualization framework for pushing behavior detection in pedestrian dynamics. Sensors 22(11):4040","journal-title":"Sensors"},{"key":"10557_CR10","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neucom.2019.06.043","volume":"361","author":"A Ali-Gombe","year":"2019","unstructured":"Ali-Gombe A, Elyan E (2019) MFC-GAN: Class-imbalanced dataset classification using multiple fake class generative adversarial network. Neurocomputing 361:212\u2013221","journal-title":"Neurocomputing"},{"key":"10557_CR11","doi-asserted-by":"crossref","first-page":"103584","DOI":"10.1016\/j.bspc.2022.103584","volume":"75","author":"A Anand","year":"2022","unstructured":"Anand A, Kadian T, Shetty MK, Gupta A (2022) Explainable AI decision model for ECG data of cardiac disorders. Biomed Signal Process Control 75:103584","journal-title":"Biomed Signal Process Control"},{"issue":"17","key":"10557_CR12","doi-asserted-by":"crossref","first-page":"2747","DOI":"10.3390\/electronics11172747","volume":"11","author":"L Bai","year":"2022","unstructured":"Bai L, Wang L, Chen T, Zhao Y, Ren H (2022) Transformer-based disease identification for small-scale imbalanced capsule endoscopy dataset. Electronics 11(17):2747","journal-title":"Electronics"},{"key":"10557_CR13","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.cose.2014.03.005","volume":"43","author":"KO Bailey","year":"2014","unstructured":"Bailey KO, Okolica JS, Peterson GL (2014) User identification and authentication using multi-modal behavioral biometrics. Comput Secur 43:77\u201389","journal-title":"Comput Secur"},{"key":"10557_CR14","doi-asserted-by":"crossref","unstructured":"Barandela R, Rosa MV, Salvador S\u00e1nchez J, Francesc JF. (2004) The imbalanced training sample problem: under or over sampling? In structural, syntactic, and statistical pattern recognition: joint IAPR international workshops, SSPR 2004 and SPR 2004, Lisbon, Portugal. Proceedings, Springer Berlin Heidelberg, pp. 806\u2013814","DOI":"10.1007\/978-3-540-27868-9_88"},{"key":"10557_CR15","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.116167","volume":"190","author":"P Bhowal","year":"2022","unstructured":"Bhowal P, Sen S, Velasquez JD, Sarkar R (2022) Fuzzy ensemble of deep learning models using choquet fuzzy integral, coalition game and information theory for breast cancer histology classification. Expert Syst Appl 190:116167","journal-title":"Expert Syst Appl"},{"key":"10557_CR16","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda M, Maki A, Mazurowski MA (2018) A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw 106:249\u2013259","journal-title":"Neural Netw"},{"key":"10557_CR17","first-page":"3","volume":"1","author":"JG Carbonell","year":"1983","unstructured":"Carbonell JG, Michalski RS, Mitchell TM (1983) An overview of machine learning. Mach Learn 1:3\u201323","journal-title":"Mach Learn"},{"key":"10557_CR18","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.neucom.2021.04.001","volume":"449","author":"M Carranza-Garc\u00eda","year":"2021","unstructured":"Carranza-Garc\u00eda M, Lara-Ben\u00edtez P, Garc\u00eda-Guti\u00e9rrez J, Riquelme JC (2021) Enhancing object detection for autonomous driving by optimizing anchor generation and addressing class imbalance. Neurocomputing 449:229\u2013244","journal-title":"Neurocomputing"},{"key":"10557_CR19","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.ejmp.2021.02.006","volume":"83","author":"I Castiglioni","year":"2021","unstructured":"Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, Gallivanone F, Cozzi A, Claudia N (2021) AI applications to medical images: from machine learning to deep learning. Phys Med 83:9\u201324","journal-title":"Phys Med"},{"key":"10557_CR20","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.eswa.2018.07.026","volume":"114","author":"E Cetinic","year":"2018","unstructured":"Cetinic E, Lipic T, Grgic S (2018) Fine-tuning convolutional neural networks for fine art classification. Expert Syst Appl 114:107\u2013118","journal-title":"Expert Syst Appl"},{"key":"10557_CR21","doi-asserted-by":"crossref","first-page":"109588","DOI":"10.1016\/j.asoc.2022.109588","volume":"129","author":"E Chamseddine","year":"2022","unstructured":"Chamseddine E, Mansouri N, Soui M, Abed M (2022) Handling class imbalance in COVID-19 chest X-ray images classification: using SMOTE and weighted loss. Appl Soft Comput 129:109588","journal-title":"Appl Soft Comput"},{"key":"10557_CR22","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Philip Kegelmeyer W (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"issue":"10","key":"10557_CR23","doi-asserted-by":"crossref","first-page":"5626","DOI":"10.1109\/TNNLS.2021.3071122","volume":"33","author":"Z Chen","year":"2021","unstructured":"Chen Z, Duan J, Kang Li, Qiu G (2021) Class-imbalanced deep learning via a class-balanced ensemble. IEEE Trans Neural Networks Learn Syst 33(10):5626\u20135640","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"10557_CR24","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.isatra.2021.07.031","volume":"126","author":"H Chen","year":"2022","unstructured":"Chen H, Li C, Yang W, Liu J, An X, Zhao Y (2022) Deep balanced cascade forest: an novel fault diagnosis method for data imbalance. ISA Trans 126:428\u2013439","journal-title":"ISA Trans"},{"key":"10557_CR25","unstructured":"Chen L-C, George P, Florian S, Hartwig A (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587"},{"key":"10557_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-022-02014-y","author":"M Ciccarelli","year":"2022","unstructured":"Ciccarelli M, Corradini F, Germani M, Menchi G, Mostarda L, Papetti A, Piangerelli M (2022) SPECTRE: a deep learning network for posture recognition in manufacturing. J Intell Manuf. https:\/\/doi.org\/10.1007\/s10845-022-02014-y","journal-title":"J Intell Manuf"},{"key":"10557_CR27","unstructured":"Codella N, Veronica R, Philipp T, Emre Celebi M, Stephen D, David G, Brian H et al. (2019) Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1902.03368"},{"key":"10557_CR28","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.biosystemseng.2019.12.003","volume":"190","author":"D Costa","year":"2020","unstructured":"Costa D, Arthur Z, Figueroa HEH, Fracarolli JA (2020) Computer vision based detection of external defects on tomatoes using deep learning. Biosyst Eng 190:131\u2013144","journal-title":"Biosyst Eng"},{"issue":"1","key":"10557_CR29","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53\u201365","journal-title":"IEEE Signal Process Mag"},{"key":"10557_CR30","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.neucom.2022.01.004","volume":"477","author":"W Dai","year":"2022","unstructured":"Dai W, Li D, Tang D, Wang H, Peng Y (2022) Deep learning approach for defective spot welds classification using small and class-imbalanced datasets. Neurocomputing 477:46\u201360","journal-title":"Neurocomputing"},{"key":"10557_CR31","unstructured":"Databases\u2013Laborat\u00f3rio Vis\u00e3o Rob\u00f3tica e Imagem, 2019, https:\/\/web.inf.ufpr.br\/vri\/databases\/. (Accessed 28 Nov 2019)."},{"key":"10557_CR32","unstructured":"Doersch C (2016) Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908"},{"key":"10557_CR33","doi-asserted-by":"crossref","unstructured":"Dong Q, Shaogang G, Xiatian Z (2017) Class rectification hard mining for imbalanced deep learning. In Proceedings of the IEEE international conference on computer vision, pp. 1851\u20131860","DOI":"10.1109\/ICCV.2017.205"},{"issue":"1","key":"10557_CR34","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3390\/app11010036","volume":"11","author":"JK Dumagpi","year":"2020","unstructured":"Dumagpi JK, Jeong Y-J (2020) Evaluating gan-based image augmentation for threat detection in large-scale x-ray security images. Appl Sci 11(1):36","journal-title":"Appl Sci"},{"key":"10557_CR35","doi-asserted-by":"crossref","first-page":"105162","DOI":"10.1016\/j.compag.2019.105162","volume":"169","author":"JGM Esgario","year":"2020","unstructured":"Esgario JGM, Krohling RA, Ventura JA (2020) Deep learning for classification and severity estimation of coffee leaf biotic stress. Comput Electron Agric 169:105162","journal-title":"Comput Electron Agric"},{"key":"10557_CR36","unstructured":"Eyepacs and Kaggle. Diabetic retinopathy detection. 2015. url: https:\/\/www.kaggle.com\/c\/diabetic-retinopathy-detection\/data"},{"issue":"12","key":"10557_CR37","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1109\/LGRS.2019.2913387","volume":"16","author":"W Feng","year":"2019","unstructured":"Feng W, Huang W, Bao W (2019) Imbalanced hyperspectral image classification with an adaptive ensemble method based on SMOTE and rotation forest with differentiated sampling rates. IEEE Geosci Remote Sens Lett 16(12):1879\u20131883","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"10557_CR38","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-98074-4","volume-title":"Learning from imbalanced data sets","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez A, Garc\u00eda S, Galar M, Prati RC, Krawczyk B, Herrera F (2018) Learning from imbalanced data sets. Springer, Cham"},{"key":"10557_CR39","doi-asserted-by":"crossref","first-page":"113275","DOI":"10.1016\/j.eswa.2020.113275","volume":"150","author":"D Fuqua","year":"2020","unstructured":"Fuqua D, Razzaghi T (2020) A cost-sensitive convolution neural network learning for control chart pattern recognition. Expert Syst Appl 150:113275","journal-title":"Expert Syst Appl"},{"key":"10557_CR40","doi-asserted-by":"crossref","unstructured":"Gandhi, Shreyansh, Samrat Kokkula, Abon Chaudhuri, Alessandro Magnani, Theban Stanley, Behzad Ahmadi, Venkatesh Kandaswamy, Omer Ovenc, and Shie Mannor. \"Scalable detection of offensive and non-compliant content\/logo in product images.\" In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2247\u20132256. 2020.","DOI":"10.1109\/WACV45572.2020.9093454"},{"key":"10557_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-022-06268-8","author":"K Ghosh","year":"2022","unstructured":"Ghosh K, Bellinger C, Corizzo R, Branco P, Krawczyk B, Japkowicz N (2022) The class imbalance problem in deep learning. Mach Learn. https:\/\/doi.org\/10.1007\/s10994-022-06268-8","journal-title":"Mach Learn"},{"issue":"11","key":"10557_CR42","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Bing Xu, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"10557_CR43","doi-asserted-by":"crossref","unstructured":"Guo X, Yilong Y, Cailing D, Gongping Y, Guangtong Z (2008) On the class imbalance problem. In 2008 Fourth international conference on natural computation, IEEE, vol. 4, pp. 192\u2013201","DOI":"10.1109\/ICNC.2008.871"},{"key":"10557_CR44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11042-021-11836-6","volume":"81","author":"H Gupta","year":"2022","unstructured":"Gupta H, Verma OP (2022) Monitoring and surveillance of urban road traffic using low altitude drone images: a deep learning approach. Multimed Tools Appl 81:1\u201321","journal-title":"Multimed Tools Appl"},{"key":"10557_CR45","doi-asserted-by":"crossref","first-page":"100057","DOI":"10.1016\/j.array.2021.100057","volume":"10","author":"A Gupta","year":"2021","unstructured":"Gupta A, Anpalagan A, Guan L, Khwaja AS (2021) Deep learning for object detection and scene perception in self-driving cars: survey, challenges, and open issues. Array 10:100057","journal-title":"Array"},{"key":"10557_CR46","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"G Haixiang","year":"2017","unstructured":"Haixiang G, Yijing Li, Jennifer Shang Gu, Mingyun HY, Bing G (2017) Learning from class-imbalanced data: review of methods and applications. Expert Syst Appl 73:220\u2013239","journal-title":"Expert Syst Appl"},{"issue":"15","key":"10557_CR47","doi-asserted-by":"crossref","first-page":"5293","DOI":"10.3390\/app10155293","volume":"10","author":"RA Hamad","year":"2020","unstructured":"Hamad RA, Yang L, Woo WL, Wei B (2020) Joint learning of temporal models to handle imbalanced data for human activity recognition. Appl Sci 10(15):5293","journal-title":"Appl Sci"},{"issue":"1","key":"10557_CR48","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","volume":"45","author":"K Han","year":"2022","unstructured":"Han K, Wang Y, Chen H, Chen X, Guo J, Liu Z, Tang Y et al (2022) A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell 45(1):87\u2013110","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10557_CR49","doi-asserted-by":"crossref","unstructured":"Han S, Chan L, Bonggeon C, Jongwuk L (2021) An empirical study for class imbalance in extreme multi-label text classification. In\u00a02021 IEEE international conference on big data and smart computing (BigComp), IEEE, pp. 338\u2013341","DOI":"10.1109\/BigComp51126.2021.00073"},{"key":"10557_CR50","unstructured":"He H, Yang B, Edwardo AG, Shutao L (2008) ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In\u00a02008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), IEEE, pp. 1322\u20131328"},{"key":"10557_CR51","doi-asserted-by":"crossref","unstructured":"He K, Xiangyu Z, Shaoqing R, Jian S (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10557_CR52","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/978-94-011-5318-8_75","volume":"1998","author":"M Heath","year":"1998","unstructured":"Heath M, Bowyer K, Kopans D, Kegelmeyer P, Moore R, Chang K, Munishkumaran S (1998) Current status of the digital database for screening mammography. Digit Mammogr Nijmegen 1998:457\u2013460","journal-title":"Digit Mammogr Nijmegen"},{"key":"10557_CR53","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.procs.2020.12.025","volume":"179","author":"AA Hidayat","year":"2021","unstructured":"Hidayat AA, Purwandari K, Cenggoro TW, Pardamean B (2021) A convolutional neural network-based ancient sundanese character classifier with data augmentation. Procedia Comput Sci 179:195\u2013201","journal-title":"Procedia Comput Sci"},{"key":"10557_CR54","doi-asserted-by":"crossref","first-page":"109174","DOI":"10.1016\/j.ymssp.2022.109174","volume":"177","author":"R Hou","year":"2022","unstructured":"Hou R, Chen J, Feng Y, Liu S, He S, Zhou Z (2022) Contrastive-weighted self-supervised model for long-tailed data classification with vision transformer augmented. Mech Syst Signal Process 177:109174","journal-title":"Mech Syst Signal Process"},{"issue":"9","key":"10557_CR55","doi-asserted-by":"crossref","first-page":"907","DOI":"10.3390\/rs9090907","volume":"9","author":"Z Huang","year":"2017","unstructured":"Huang Z, Pan Z, Lei B (2017b) Transfer learning with deep convolutional neural network for SAR target classification with limited labeled data. Remote Sensing 9(9):907","journal-title":"Remote Sensing"},{"key":"10557_CR56","doi-asserted-by":"crossref","first-page":"88399","DOI":"10.1109\/ACCESS.2020.2992683","volume":"8","author":"Y Huang","year":"2020","unstructured":"Huang Y, Jin Yi, Li Y, Lin Z (2020) Towards imbalanced image classification: a generative adversarial network ensemble learning method. IEEE Access 8:88399\u201388409","journal-title":"IEEE Access"},{"key":"10557_CR57","doi-asserted-by":"crossref","unstructured":"Huang G, Zhuang L, Laurens Van Der M, Kilian QW (2017a) Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"10557_CR58","doi-asserted-by":"crossref","unstructured":"Hung J, Anne C (2017) Applying faster R-CNN for object detection on malaria images. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 56\u201361","DOI":"10.1109\/CVPRW.2017.112"},{"key":"10557_CR59","doi-asserted-by":"crossref","unstructured":"Hussain E, Mahmudul H, Syed ZH, Tanzina HA, Md Anisur R, Mohammad ZP (2020) Deep learning based binary classification for alzheimer\u2019s disease detection using brain MRI images. In 2020 15th IEEE conference on industrial electronics and applications (ICIEA), IEEE, pp. 1115\u20131120","DOI":"10.1109\/ICIEA48937.2020.9248213"},{"key":"10557_CR60","doi-asserted-by":"crossref","DOI":"10.1016\/j.cosrev.2023.100553","volume":"48","author":"G Iglesias","year":"2023","unstructured":"Iglesias G, Talavera E, D\u00edaz-\u00c1lvarez A (2023) A survey on GANs for computer vision: recent research, analysis and taxonomy. Comput Sci Rev 48:100553","journal-title":"Comput Sci Rev"},{"key":"10557_CR61","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1002\/9781118646106.ch8","volume-title":"Imbalanced learning: Foundations, algorithms, and applications","author":"N Japkowicz","year":"2013","unstructured":"Japkowicz N (2013) Assessment metrics for imbalanced learning. Imbalanced learning: Foundations, algorithms, and applications. John Wiley & Sons, Hoboken, pp 187\u2013206"},{"issue":"1\u20132","key":"10557_CR62","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1177\/0040517520928604","volume":"92","author":"J Jing","year":"2022","unstructured":"Jing J, Wang Z, R\u00e4tsch M, Zhang H (2022) Mobile-Unet: an efficient convolutional neural network for fabric defect detection. Text Res J 92(1\u20132):30\u201342","journal-title":"Text Res J"},{"issue":"1","key":"10557_CR63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-018-0162-3","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson JM, Khoshgoftaar TM (2019) Survey on deep learning with class imbalance. J Big Data 6(1):1\u201354","journal-title":"J Big Data"},{"key":"10557_CR64","doi-asserted-by":"crossref","first-page":"3299","DOI":"10.1109\/TNNLS.2022.3144791","volume":"34","author":"M Kaselimi","year":"2022","unstructured":"Kaselimi M, Voulodimos A, Daskalopoulos I, Doulamis N, Doulamis A (2022) A vision transformer model for convolution-free multilabel classification of satellite imagery in deforestation monitoring. IEEE Trans Neural Networks Learn Syst 34:3299","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"4","key":"10557_CR65","first-page":"1","volume":"52","author":"H Kaur","year":"2019","unstructured":"Kaur H, Pannu HS, Malhi AK (2019) A systematic review on imbalanced data challenges in machine learning: applications and solutions. ACM Comput Surv (CSUR) 52(4):1\u201336","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"10","key":"10557_CR66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","volume":"54","author":"S Khan","year":"2022","unstructured":"Khan S, Naseer M, Hayat M, Zamir SW, Khan FS, Shah M (2022) Transformers in vision: a survey. ACM Comput Surv (CSUR) 54(10):1\u201341","journal-title":"ACM Comput Surv (CSUR)"},{"key":"10557_CR67","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.patrec.2021.07.017","volume":"151","author":"Y Kim","year":"2021","unstructured":"Kim Y, Lee Y, Jeon M (2021) Imbalanced image classification with complement cross entropy. Pattern Recogn Lett 151:33\u201340","journal-title":"Pattern Recogn Lett"},{"key":"10557_CR68","doi-asserted-by":"crossref","unstructured":"Kim P-K, Kil-Taek L. (2017) Vehicle type classification using bagging and convolutional neural network on multi view surveillance image. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 41\u201346","DOI":"10.1109\/CVPRW.2017.126"},{"issue":"1","key":"10557_CR69","first-page":"25","volume":"30","author":"S Kotsiantis","year":"2006","unstructured":"Kotsiantis S, Kanellopoulos D, Pintelas P (2006) Handling imbalanced datasets: a review. GESTS Int Trans Comput Sci Eng 30(1):25\u201336","journal-title":"GESTS Int Trans Comput Sci Eng"},{"issue":"4","key":"10557_CR70","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","volume":"5","author":"B Krawczyk","year":"2016","unstructured":"Krawczyk B (2016) Learning from imbalanced data: open challenges and future directions. Progress Artif Intell 5(4):221\u2013232","journal-title":"Progress Artif Intell"},{"key":"10557_CR71","unstructured":"Kulatilleke, Gayan K (2022) Challenges and complexities in machine learning based credit card fraud detection. arXiv preprint arXiv:2208.10943"},{"key":"10557_CR72","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1007\/978-3-030-00931-1_82","volume-title":"Medical image computing and computer assisted intervention\u2013MICCAI 2018","author":"W Kuo","year":"2018","unstructured":"Kuo W, H\u00e4ne C, Yuh E, Mukherjee P, Malik J (2018) Cost-sensitive active learning for intracranial hemorrhage detection. Medical image computing and computer assisted intervention\u2013MICCAI 2018. Springer International Publishing, Cham, pp 715\u2013723"},{"issue":"7553","key":"10557_CR73","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"1","key":"10557_CR74","first-page":"559","volume":"18","author":"G Lema\u00eetre","year":"2017","unstructured":"Lema\u00eetre G, Nogueira F, Aridas CK (2017) Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J Mach Learn Res 18(1):559\u2013563","journal-title":"J Mach Learn Res"},{"key":"10557_CR75","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.ins.2019.06.011","volume":"501","author":"T Li","year":"2019","unstructured":"Li T, Gao Y, Wang K, Guo S, Liu H, Kang H (2019) Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Inf Sci 501:511\u2013522","journal-title":"Inf Sci"},{"issue":"1","key":"10557_CR76","doi-asserted-by":"crossref","first-page":"173","DOI":"10.3390\/s22010173","volume":"22","author":"L Li","year":"2022","unstructured":"Li L, Zhang S, Wang B (2022) Apple leaf disease identification with a small and imbalanced dataset based on lightweight convolutional networks. Sensors 22(1):173","journal-title":"Sensors"},{"key":"10557_CR77","unstructured":"Li D, Zhang Z, Xiaotang C, Haibin L, Kaiqi H (2016) A richly annotated dataset for pedestrian attribute recognition. arXiv preprint arXiv:1603.07054"},{"key":"10557_CR78","doi-asserted-by":"crossref","unstructured":"Li Y, Tao W, Bingyi K, Sheng T, Chunfeng W, Jintao L, Jiashi F (2020) Overcoming classifier imbalance for long-tail object detection with balanced group softmax. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 10991\u201311000","DOI":"10.1109\/CVPR42600.2020.01100"},{"key":"10557_CR79","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Priya G, Ross G, Kaiming H, Piotr D (2017) Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"10557_CR80","doi-asserted-by":"crossref","first-page":"24417","DOI":"10.1109\/ACCESS.2017.2766203","volume":"5","author":"W Liu","year":"2017","unstructured":"Liu W, Zhang M, Luo Z, Cai Y (2017) An ensemble deep learning method for vehicle type classification on visual traffic surveillance sensors. IEEE Access 5:24417\u201324425","journal-title":"IEEE Access"},{"issue":"7","key":"10557_CR81","doi-asserted-by":"crossref","first-page":"4681","DOI":"10.1109\/TIM.2019.2957849","volume":"69","author":"Y Liu","year":"2019","unstructured":"Liu Y, Gao H, Guo L, Qin A, Cai C, You Z (2019) A data-flow oriented deep ensemble learning method for real-time surface defect inspection. IEEE Trans Instrum Meas 69(7):4681\u20134691","journal-title":"IEEE Trans Instrum Meas"},{"key":"10557_CR82","doi-asserted-by":"crossref","unstructured":"Liu Z, Ping L, Xiaogang W, Xiaoou T (2015) Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pp. 3730\u20133738","DOI":"10.1109\/ICCV.2015.425"},{"key":"10557_CR83","doi-asserted-by":"crossref","unstructured":"Liu Z, Yutong L, Yue C, Han H, Yixuan W, Zheng Z, Stephen L, Baining G (2021) Swin transformer: hierarchical vision transformer using shifted windows. In Proceedings of the IEEE\/CVF international conference on computer vision, pp. 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10557_CR84","doi-asserted-by":"crossref","unstructured":"Lu X, Chao M, Bingbing N, Xiaokang Y, Ian R, Ming-Hsuan Y (2018) Deep regression tracking with shrinkage loss. In Proceedings of the European conference on computer vision (ECCV), pp. 353\u2013369","DOI":"10.1007\/978-3-030-01264-9_22"},{"key":"10557_CR85","doi-asserted-by":"crossref","first-page":"381","DOI":"10.21275\/ART20203995","volume":"9","author":"B Mahesh","year":"2020","unstructured":"Mahesh B (2020) Machine learning algorithms-a review. Int J Sci Res (IJSR) 9:381\u2013386","journal-title":"Int J Sci Res (IJSR)"},{"key":"10557_CR86","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk A, Micha\u0142 G (2018) Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW), IEEE, pp. 117\u2013122","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"10557_CR87","doi-asserted-by":"crossref","unstructured":"Milletari F, Nassir N, Seyed-Ahmad A (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In\u00a02016 fourth international conference on 3D vision (3DV), IEEE, pp. 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"issue":"7","key":"10557_CR88","first-page":"3523","volume":"44","author":"S Minaee","year":"2021","unstructured":"Minaee S, Boykov Y, Porikli F, Plaza A, Kehtarnavaz N, Terzopoulos D (2021) Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Mach Intell 44(7):3523\u20133542","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10557_CR89","unstructured":"MobileODT, \u201cIntel and mobileodt cervical can cer screening.\u201d [Online]. Available: https:\/\/www.kaggle.com\/c\/intel-mobileodt-cervical-cancer-screening\/data"},{"key":"10557_CR90","unstructured":"Mooney P (2020) Breast histopathology images. Kaggle. Accessed Oct 09, 2020. https:\/\/www.kaggle.com\/datasets\/paultimothymooney\/breast-histopathology-images"},{"key":"10557_CR91","doi-asserted-by":"crossref","first-page":"107581","DOI":"10.1016\/j.apacoust.2020.107581","volume":"172","author":"Z Mushtaq","year":"2021","unstructured":"Mushtaq Z, Shun-Feng Su, Tran Q-V (2021) Spectral images based environmental sound classification using CNN with meaningful data augmentation. Appl Acoust 172:107581","journal-title":"Appl Acoust"},{"issue":"2","key":"10557_CR92","first-page":"46","volume":"8","author":"H Nazki","year":"2019","unstructured":"Nazki H, Lee J, Yoon S, Park DS (2019) Image-to-image translation with GAN for synthetic data augmentation in plant disease datasets. Smart Med J 8(2):46\u201357","journal-title":"Smart Med J"},{"key":"10557_CR93","unstructured":"Nesteruk S, Dmitrii S, Mariia P (2021) Image augmentation for multitask few-shot learning: Agricultural domain use-case. arXiv preprint arXiv:2102.12295"},{"issue":"9","key":"10557_CR94","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.3390\/s20092639","volume":"20","author":"QT Ngo","year":"2020","unstructured":"Ngo QT, Yoon S (2020) Facial expression recognition based on weighted-cluster loss and deep transfer learning using a highly imbalanced dataset. Sensors 20(9):2639","journal-title":"Sensors"},{"key":"10557_CR95","volume-title":"COVID-19 X-ray Image classification: a transfer learning approach","author":"JAA Ortiz","year":"2021","unstructured":"Ortiz JAA (2021) COVID-19 X-ray Image classification: a transfer learning approach. University of California, Los Angeles"},{"key":"10557_CR96","first-page":"125","volume":"23","author":"ND Papathanasiou","year":"2020","unstructured":"Papathanasiou ND, Spyridonidis T, Apostolopoulos DJ (2020) Automatic characterization of myocardial perfusion imaging polar maps employing deep learning and data augmentation. Hell J Nucl Med 23:125\u2013132","journal-title":"Hell J Nucl Med"},{"key":"10557_CR97","doi-asserted-by":"crossref","unstructured":"Park S, Jongin L, Younghan J, Jin YC (2021) Influence-balanced loss for imbalanced visual classification. In Proceedings of the IEEE\/CVF international conference on computer vision, pp. 735\u2013744","DOI":"10.1109\/ICCV48922.2021.00077"},{"key":"10557_CR98","doi-asserted-by":"crossref","first-page":"105590","DOI":"10.1016\/j.knosys.2020.105590","volume":"194","author":"F P\u00e9rez-Hern\u00e1ndez","year":"2020","unstructured":"P\u00e9rez-Hern\u00e1ndez F, Tabik S, Lamas A, Olmos R, Fujita H, Herrera F (2020) Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly: application in video surveillance. Knowl-Based Syst 194:105590","journal-title":"Knowl-Based Syst"},{"key":"10557_CR99","unstructured":"Phan TH, Kazuma Y (2020) Resolving class imbalance in object detection with weighted cross entropy losses. arXiv preprint arXiv:2006.01413"},{"issue":"01","key":"10557_CR100","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1142\/S1793351X17400050","volume":"11","author":"S Pouyanfar","year":"2017","unstructured":"Pouyanfar S, Chen S-C (2017) Automatic video event detection for imbalance data using enhanced ensemble deep learning. Int J Semant Comput 11(01):85\u2013109","journal-title":"Int J Semant Comput"},{"issue":"4","key":"10557_CR101","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.3390\/molecules26041111","volume":"26","author":"A R\u00e1cz","year":"2021","unstructured":"R\u00e1cz A, Bajusz D, H\u00e9berger K (2021) Effect of dataset size and train\/test split ratios in QSAR\/QSPR multiclass classification. Molecules 26(4):1111","journal-title":"Molecules"},{"issue":"2","key":"10557_CR102","doi-asserted-by":"crossref","first-page":"224","DOI":"10.7763\/IJMLC.2013.V3.307","volume":"3","author":"MM Rahman","year":"2013","unstructured":"Rahman MM, Davis DN (2013) Addressing the class imbalance problem in medical datasets. Int J Mach Learn Comput 3(2):224","journal-title":"Int J Mach Learn Comput"},{"key":"10557_CR103","doi-asserted-by":"crossref","first-page":"102820","DOI":"10.1016\/j.bspc.2021.102820","volume":"68","author":"A Rath","year":"2021","unstructured":"Rath A, Mishra D, Panda G, Satapathy SC (2021) Heart disease detection using deep learning methods from imbalanced ECG samples. Biomed Signal Process Control 68:102820","journal-title":"Biomed Signal Process Control"},{"key":"10557_CR104","unstructured":"Reddy C, Deepak S, Soroush M, Adriana R-S, Samira S, Sina H (2021) Benchmarking bias mitigation algorithms in representation learning through fairness metrics. In thirty-fifth conference on neural information processing systems datasets and benchmarks track (Round 1). url: https:\/\/paperswithcode.com\/dataset\/ci-mnist"},{"key":"10557_CR105","doi-asserted-by":"crossref","unstructured":"Redmon J, Santosh D, Ross G, Ali F (2016) You only look once: unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"10557_CR106","first-page":"1","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inform Process Syst 28:1","journal-title":"Adv Neural Inform Process Syst"},{"key":"10557_CR107","doi-asserted-by":"crossref","unstructured":"Ren J, Mingyuan Z, Cunjun Y, Ziwei L (2022) Balanced mse for imbalanced visual regression. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7926\u20137935","DOI":"10.1109\/CVPR52688.2022.00777"},{"key":"10557_CR108","doi-asserted-by":"crossref","unstructured":"Reza MS, Ma J (2018) Imbalanced histopathological breast cancer image classification with convolutional neural network. In 2018 14th IEEE international conference on signal processing (ICSP), IEEE, pp. 619\u2013624","DOI":"10.1109\/ICSP.2018.8652304"},{"key":"10557_CR109","doi-asserted-by":"crossref","unstructured":"Rezaei M, Haojin Y, Christoph M (2018) Conditional generative refinement adversarial networks for unbalanced medical image semantic segmentation. arXiv preprint arXiv:1810.03871","DOI":"10.1109\/WACV.2019.00200"},{"key":"10557_CR110","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Philipp F, Thomas B (2015) U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18. Springer International Publishing, pp. 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10557_CR111","doi-asserted-by":"crossref","unstructured":"Saini M, Seba S (2022c) Cervical cancer screening on multi-class imbalanced cervigram dataset using transfer learning. In 2022c 15th International congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), IEEE, pp. 1\u20136","DOI":"10.1109\/CISP-BMEI56279.2022.9980238"},{"key":"10557_CR112","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1007\/978-3-030-31332-6_36","volume-title":"Pattern recognition and image analysis","author":"M Saini","year":"2019","unstructured":"Saini M, Susan S (2019) Data augmentation of minority class with transfer learning for classification of imbalanced breast cancer dataset using inception-V3. Pattern recognition and image analysis. Springer International Publishing, Cham, pp 409\u2013420"},{"key":"10557_CR113","doi-asserted-by":"crossref","first-page":"106759","DOI":"10.1016\/j.asoc.2020.106759","volume":"97","author":"M Saini","year":"2020","unstructured":"Saini M, Susan S (2020) Deep transfer with minority data augmentation for imbalanced breast cancer dataset. Appl Soft Comput 97:106759","journal-title":"Appl Soft Comput"},{"issue":"14","key":"10557_CR114","doi-asserted-by":"crossref","first-page":"20821","DOI":"10.1007\/s11042-021-10612-w","volume":"80","author":"M Saini","year":"2021","unstructured":"Saini M, Susan S (2021) Bag-of-visual-words codebook generation using deep features for effective classification of imbalanced multi-class image datasets. Multimed Tools Appl 80(14):20821\u201320847","journal-title":"Multimed Tools Appl"},{"key":"10557_CR115","doi-asserted-by":"crossref","first-page":"105989","DOI":"10.1016\/j.compbiomed.2022.105989","volume":"149","author":"M Saini","year":"2022","unstructured":"Saini M, Susan S (2022a) Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets. Comput Biol Med 149:105989","journal-title":"Comput Biol Med"},{"key":"10557_CR116","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1109\/TCBB.2022.3163277","volume":"20","author":"M Saini","year":"2022","unstructured":"Saini M, Susan S (2022b) Vggin-net: deep transfer network for imbalanced breast cancer dataset. IEEE\/ACM Trans Comput Biol Bioinform 20:752","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"1","key":"10557_CR117","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.eij.2020.02.007","volume":"22","author":"GAOGD Sambasivam","year":"2021","unstructured":"Sambasivam GAOGD, Opiyo GD (2021) A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egypt Inform J 22(1):27\u201334","journal-title":"Egypt Inform J"},{"key":"10557_CR118","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00414-0","volume":"8","author":"V Sampath","year":"2021","unstructured":"Sampath V, Maurtua I, Martin JJA, Gutierrez A (2021) A survey on generative adversarial networks for imbalance problems in computer vision tasks. J Big Data 8:1\u201359","journal-title":"J Big Data"},{"key":"10557_CR119","doi-asserted-by":"crossref","unstructured":"Sandler M, Andrew H, Menglong Z, Andrey Z, Liang-Chieh C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"1","key":"10557_CR120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12880-020-00529-5","volume":"21","author":"A Saood","year":"2021","unstructured":"Saood A, Hatem I (2021) COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med Imaging 21(1):1\u201310","journal-title":"BMC Med Imaging"},{"key":"10557_CR121","doi-asserted-by":"crossref","unstructured":"Sarafianos N, Xiang X, Ioannis AK (2018) Deep imbalanced attribute classification using visual attention aggregation. In Proceedings of the European conference on computer vision (ECCV), pp. 680\u2013697","DOI":"10.1007\/978-3-030-01252-6_42"},{"issue":"1","key":"10557_CR122","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","volume":"40","author":"C Seiffert","year":"2009","unstructured":"Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A (2009) RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern Part a: Syst Humans 40(1):185\u2013197","journal-title":"IEEE Trans Syst Man Cybern Part a: Syst Humans"},{"key":"10557_CR123","doi-asserted-by":"crossref","first-page":"102802","DOI":"10.1016\/j.media.2023.102802","volume":"88","author":"F Shamshad","year":"2023","unstructured":"Shamshad F, Khan S, Zamir SW, Khan MH, Hayat M, Khan FS, Huazhu F (2023) Transformers in medical imaging: a survey. Med Image Anal 88:102802","journal-title":"Med Image Anal"},{"key":"10557_CR124","doi-asserted-by":"crossref","unstructured":"Shao S, Zeming L, Tianyuan Z, Chao P, Gang Y, Xiangyu Z, Jing L, Jian S (2019) Objects365: a large-scale, high-quality dataset for object detection. In Proceedings of the IEEE\/CVF international conference on computer vision, pp. 8430\u20138439. url: https:\/\/github.com\/nkicsl\/DDR-dataset","DOI":"10.1109\/ICCV.2019.00852"},{"issue":"10","key":"10557_CR125","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.3390\/en13102509","volume":"13","author":"K Shaukat","year":"2020","unstructured":"Shaukat K, Luo S, Varadharajan V, Hameed IA, Chen S, Liu D, Li J (2020a) Performance comparison and current challenges of using machine learning techniques in cybersecurity. Energies 13(10):2509","journal-title":"Energies"},{"key":"10557_CR126","doi-asserted-by":"crossref","first-page":"222310","DOI":"10.1109\/ACCESS.2020.3041951","volume":"8","author":"K Shaukat","year":"2020","unstructured":"Shaukat K, Luo S, Varadharajan V, Hameed IA, Min Xu (2020b) A survey on machine learning techniques for cyber security in the last decade. IEEE Access 8:222310\u2013222354","journal-title":"IEEE Access"},{"key":"10557_CR127","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106030","volume":"122","author":"K Shaukat","year":"2023","unstructured":"Shaukat K, Luo S, Varadharajan V (2023) A novel deep learning-based approach for malware detection. Eng Appl Artif Intell 122:106030","journal-title":"Eng Appl Artif Intell"},{"key":"10557_CR128","doi-asserted-by":"crossref","unstructured":"Shauka K, Luo S, Chen S, Liu D, (2020c) Cyber threat detection using machine learning techniques: a performance evaluation perspective. In 2020c international conference on cyber warfare and security (ICCWS), IEEE, pp. 1\u20136","DOI":"10.1109\/ICCWS48432.2020.9292388"},{"key":"10557_CR129","doi-asserted-by":"crossref","unstructured":"Shaukat K, Suhuai L, Nadir A, Talha MA, Muhammad ET, Ibrahim AH (2021) An analysis of blessed Friday sale at a retail store using classification models. In 2021 The 4th international conference on software engineering and information management, pp. 193\u2013198","DOI":"10.1145\/3451471.3451502"},{"key":"10557_CR130","unstructured":"Simonyan K, Andrew Z (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"10557_CR131","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.neunet.2023.01.015","volume":"161","author":"AK Sivapuram","year":"2023","unstructured":"Sivapuram AK, Ravi V, Senthil G, Gorthi RK (2023) Visal\u2014a novel learning strategy to address class imbalance. Neural Netw 161:178","journal-title":"Neural Netw"},{"key":"10557_CR132","doi-asserted-by":"crossref","unstructured":"Soleymani M, Mahdi B, Hadi M, Farnad N (2021) Construction material classification on imbalanced datasets using vision transformer (ViT) architecture. arXiv preprint arXiv:2108.09527","DOI":"10.21203\/rs.3.rs-1948162\/v1"},{"key":"10557_CR133","first-page":"1","volume":"15","author":"WJ Sori","year":"2021","unstructured":"Sori WJ, Feng J, Godana AW, Liu S, Gelmecha DJ (2021) DFD-Net: lung cancer detection from denoised CT scan image using deep learning. Front Comp Sci 15:1\u201313","journal-title":"Front Comp Sci"},{"key":"10557_CR134","doi-asserted-by":"crossref","unstructured":"Sozykin K, Stanislav P, Adil K, Rasheed H, Jooyoung L (2018) Multi-label class-imbalanced action recognition in hockey videos via 3D convolutional neural networks. In\u00a02018 19th IEEE\/ACIS international conference on software engineering, artificial intelligence, networking and parallel\/distributed computing (SNPD), IEEE, pp. 146\u2013151","DOI":"10.1109\/SNPD.2018.8441034"},{"key":"10557_CR135","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/3264367","volume":"2022","author":"C Srinivas","year":"2022","unstructured":"Srinivas C, Nandini Prasad KS, Zakariah M, Alothaibi YA, Kamran Shaukat B (2022) Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images. J Healthcare Eng 2022:1\u201317","journal-title":"J Healthcare Eng"},{"issue":"12","key":"10557_CR136","doi-asserted-by":"crossref","first-page":"3358","DOI":"10.1016\/j.patcog.2007.04.009","volume":"40","author":"Y Sun","year":"2007","unstructured":"Sun Y, Kamel MS, Wong AKC, Wang Y (2007) Cost-sensitive boosting for classification of imbalanced data. Pattern Recognit 40(12):3358\u20133378","journal-title":"Pattern Recognit"},{"issue":"3","key":"10557_CR137","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3190618","volume":"51","author":"K Sundararajan","year":"2018","unstructured":"Sundararajan K, Woodard DL (2018) Deep learning for biometrics: a survey. ACM Comput Surv (CSUR) 51(3):1\u201334","journal-title":"ACM Comput Surv (CSUR)"},{"key":"10557_CR138","first-page":"64","volume-title":"Database systems for advanced applications","author":"S Susan","year":"2022","unstructured":"Susan S, Ashu K (2022) Localized metric learning for large multi-class extremely imbalanced face database. Database systems for advanced applications. Springer International Publishing, Cham, pp 64\u201378"},{"key":"10557_CR139","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.asoc.2019.02.028","volume":"78","author":"S Susan","year":"2019","unstructured":"Susan S, Kumar A (2019) SSOMaj-SMOTE-SSOMin: three-step intelligent pruning of majority and minority samples for learning from imbalanced datasets. Appl Soft Comput 78:141\u2013149","journal-title":"Appl Soft Comput"},{"issue":"4","key":"10557_CR140","doi-asserted-by":"crossref","first-page":"e12298","DOI":"10.1002\/eng2.12298","volume":"3","author":"S Susan","year":"2021","unstructured":"Susan S, Kumar A (2021) The balancing trick: optimized sampling of imbalanced datasets\u2014a brief survey of the recent state of the art. Eng Rep 3(4):e12298","journal-title":"Eng Rep"},{"key":"10557_CR141","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1007\/978-981-15-5148-2_71","volume-title":"International conference on innovative computing and communications","author":"S Susan","year":"2021","unstructured":"Susan S, Sethi D, Arora K (2021) CW-CAE: pulmonary nodule detection from imbalanced dataset using class-weighted convolutional autoencoder. International conference on innovative computing and communications. Springer Singapore, Singapore, pp 825\u2013833"},{"key":"10557_CR142","unstructured":"Susan S, Ankit K (2016) Auto-segmentation using mean-shift and entropy analysis. In\u00a02016 3rd international conference on computing for sustainable global development (INDIACom), IEEE, pp. 292\u2013296"},{"key":"10557_CR143","doi-asserted-by":"crossref","unstructured":"Susan S, Amitesh K (2020) Hybrid of intelligent minority oversampling and PSO-based intelligent majority undersampling for learning from imbalanced datasets. In\u00a0Intelligent systems design and applications: 18th international conference on intelligent systems design and applications (ISDA 2018) held in Vellore, India, December 6\u20138, Springer International Publishing, Vol. 2, pp. 760\u2013769","DOI":"10.1007\/978-3-030-16660-1_74"},{"key":"10557_CR144","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231","author":"C Szegedy","year":"2017","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. Proc AAAI Conf Artif Intell. https:\/\/doi.org\/10.1609\/aaai.v31i1.11231","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"10557_CR145","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vincent V, Sergey I, Jon S, Zbigniew W (2016) Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"issue":"5","key":"10557_CR146","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh N, Shin JY, Gurudu SR, Todd Hurst R, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299\u20131312","journal-title":"IEEE Trans Med Imaging"},{"key":"10557_CR147","unstructured":"Tan M, Quoc L (2019) Efficientnet: rethinking model scaling for convolutional neural networks.\" In International conference on machine learning, PMLR, pp. 6105\u20136114"},{"key":"10557_CR148","doi-asserted-by":"crossref","unstructured":"Tan M, Ruoming P, Quoc VL. (2020) Efficientdet: scalable and efficient object detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 10781\u201310790","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"10557_CR149","unstructured":"Tanaka FHKS, Claus A (2019) Data augmentation using GANs. arXiv preprint arXiv:1904.09135"},{"issue":"1","key":"10557_CR150","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00349-y","volume":"7","author":"J Tanha","year":"2020","unstructured":"Tanha J, Abdi Y, Samadi N, Razzaghi N, Asadpour M (2020) Boosting methods for multi-class imbalanced data classification: an experimental review. J Big Data 7(1):1\u201347","journal-title":"J Big Data"},{"key":"10557_CR151","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/5156416","volume":"2019","author":"GS Tran","year":"2019","unstructured":"Tran GS, Nghiem TP, Nguyen VT, Luong CM, Burie J-C (2019) Improving accuracy of lung nodule classification using deep learning with focal loss. J Healthcare Eng 2019:1\u20139","journal-title":"J Healthcare Eng"},{"key":"10557_CR152","doi-asserted-by":"crossref","first-page":"105506","DOI":"10.1016\/j.compag.2020.105506","volume":"175","author":"VH Trong","year":"2020","unstructured":"Trong VH, Gwang-hyun Y, Dang Thanh V, Jin-young K (2020) Late fusion of multimodal deep neural networks for weeds classification. Comput Electron Agric 175:105506","journal-title":"Comput Electron Agric"},{"key":"10557_CR153","doi-asserted-by":"crossref","unstructured":"Vicente S, Joao C, Lourdes A, Jorge B (2014) Reconstructing pascal voc. In\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 41\u201348","DOI":"10.1109\/CVPR.2014.13"},{"key":"10557_CR154","doi-asserted-by":"crossref","unstructured":"Wang Y, Weihao G, Jie Y, Wei W, Junjie Y (2019) Dynamic curriculum learning for imbalanced data classification. In Proceedings of the IEEE\/CVF international conference on computer vision, pp. 5017\u20135026","DOI":"10.1109\/ICCV.2019.00512"},{"key":"10557_CR155","doi-asserted-by":"crossref","first-page":"103790","DOI":"10.1016\/j.dsp.2022.103790","volume":"132","author":"G Wang","year":"2022","unstructured":"Wang G, Ding H, Duan M, Yuanyuan Pu, Yang Z, Li H (2022) Fighting against terrorism: a real-time CCTV autonomous weapons detection based on improved YOLO v4. Digital Signal Process 132:103790","journal-title":"Digital Signal Process"},{"key":"10557_CR156","doi-asserted-by":"crossref","unstructured":"Wardhani NWS, Masithoh YR, Atiek I, Agus DS, Prayudi L (2019) Cross-validation metrics for evaluating classification performance on imbalanced data. In 2019 International conference on computer, control, informatics and its applications (IC3INA), IEEE, pp. 14\u201318","DOI":"10.1109\/IC3INA48034.2019.8949568"},{"key":"10557_CR157","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.future.2022.12.004","volume":"141","author":"M Wo\u017aniak","year":"2023","unstructured":"Wo\u017aniak M, Wieczorek M, Si\u0142ka J (2023) BiLSTM deep neural network model for imbalanced medical data of IoT systems. Futur Gener Comput Syst 141:489\u2013499","journal-title":"Futur Gener Comput Syst"},{"key":"10557_CR158","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/978-3-030-00946-5_11","volume-title":"Image analysis for moving organ, breast, and thoracic images","author":"E Wu","year":"2018","unstructured":"Wu E, Kevin W, Cox D, Lotter W (2018) Conditional infilling GANs for data augmentation in mammogram classification. Image analysis for moving organ, breast, and thoracic images. Springer, Cham, pp 98\u2013106"},{"key":"10557_CR159","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/978-3-030-32236-6_51","volume-title":"Natural language processing and chinese computing","author":"F Xu","year":"2019","unstructured":"Xu F, Uszkoreit H, Yangzhou D, Fan W, Zhao D, Zhu J (2019) Explainable AI: a brief survey on history, research areas, approaches and challenges. Natural language processing and chinese computing. Springer International Publishing, Dunhuang, pp 563\u2013574"},{"key":"10557_CR160","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TIP.2021.3050677","volume":"30","author":"B Xu","year":"2021","unstructured":"Xu B, Zeng Z, Lian C, Ding Z (2021) Semi-supervised low-rank semantics grouping for zero-shot learning. IEEE Trans Image Process 30:2207\u20132219","journal-title":"IEEE Trans Image Process"},{"key":"10557_CR161","doi-asserted-by":"crossref","first-page":"109347","DOI":"10.1016\/j.patcog.2023.109347","volume":"137","author":"M Xu","year":"2023","unstructured":"Xu M, Yoon S, Fuentes A, Park DS (2023) A comprehensive survey of image augmentation techniques for deep learning. Pattern Recogn 137:109347","journal-title":"Pattern Recogn"},{"issue":"17","key":"10557_CR162","doi-asserted-by":"crossref","first-page":"8707","DOI":"10.3390\/app12178707","volume":"12","author":"L Yang","year":"2022","unstructured":"Yang L, Yuan G, Zhou H, Liu H, Chen J, Hao Wu (2022) RS-YOLOX: a high-precision detector for object detection in satellite remote sensing images. Appl Sci 12(17):8707","journal-title":"Appl Sci"},{"key":"10557_CR163","doi-asserted-by":"crossref","unstructured":"Yang X, Matloob K, Kamran S (2020) Biomarker CA125 feature engineering and class imbalance learning improves ovarian cancer prediction. In 2020 IEEE Asia-Pacific conference on computer science and data engineering (CSDE), IEEE, pp. 1\u20136","DOI":"10.1109\/CSDE50874.2020.9411607"},{"key":"10557_CR164","doi-asserted-by":"crossref","first-page":"102026","DOI":"10.1016\/j.compmedimag.2021.102026","volume":"95","author":"M Yeung","year":"2022","unstructured":"Yeung M, Sala E, Sch\u00f6nlieb C-B, Rundo L (2022) Unified focal loss: generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Comput Med Imaging Graph 95:102026","journal-title":"Comput Med Imaging Graph"},{"key":"10557_CR165","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.knosys.2015.11.013","volume":"94","author":"Li Yijing","year":"2016","unstructured":"Yijing Li, Haixiang G, Xiao L, Yanan Li, Jinling Li (2016) Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data. Knowl-Based Syst 94:88\u2013104","journal-title":"Knowl-Based Syst"},{"key":"10557_CR166","doi-asserted-by":"crossref","first-page":"103514","DOI":"10.1016\/j.dsp.2022.103514","volume":"126","author":"SS Zaidi","year":"2022","unstructured":"Zaidi SS, Abbas MS, Ansari AA, Kanwal N, Asghar M, Lee B (2022) A survey of modern deep learning based object detection models. Digit Signal Process 126:103514","journal-title":"Digit Signal Process"},{"key":"10557_CR167","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1016\/j.procs.2015.09.027","volume":"65","author":"NM Zaitoun","year":"2015","unstructured":"Zaitoun NM, Aqel MJ (2015) Survey on image segmentation techniques. Procedia Comput Sci 65:797\u2013806","journal-title":"Procedia Comput Sci"},{"issue":"9","key":"10557_CR168","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1177\/0037549716666962","volume":"92","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Siyuan Lu, Zhou X, Yang M, Lenan Wu, Liu B, Phillips P, Wang S (2016) Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. SIMULATION 92(9):861\u2013871","journal-title":"SIMULATION"},{"key":"10557_CR169","doi-asserted-by":"crossref","first-page":"2778","DOI":"10.1109\/JSTARS.2020.2995703","volume":"13","author":"L Zhang","year":"2020","unstructured":"Zhang L, Zhang C, Quan S, Xiao H, Kuang G, Liu Li (2020) A class imbalance loss for imbalanced object recognition. IEEE J Select Top Appl Earth Observ Remote Sens 13:2778\u20132792","journal-title":"IEEE J Select Top Appl Earth Observ Remote Sens"},{"key":"10557_CR170","unstructured":"Zhang Y, Bingyi K, Bryan H, Shuicheng Y, Jiashi F (2021) Deep long-tailed learning: a survey. arXiv preprint arXiv:2110.04596"},{"issue":"17","key":"10557_CR171","doi-asserted-by":"crossref","first-page":"24265","DOI":"10.1007\/s11042-022-12670-0","volume":"81","author":"C Zhao","year":"2022","unstructured":"Zhao C, Shuai R, Ma Li, Liu W, Menglin Wu (2022) Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT. Multimed Tools Appl 81(17):24265\u201324300","journal-title":"Multimed Tools Appl"},{"key":"10557_CR172","doi-asserted-by":"crossref","unstructured":"Zhao H, Jianping S, Xiaojuan Q, Xiaogang W, Jiaya J (2017) Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"key":"10557_CR173","doi-asserted-by":"crossref","unstructured":"Zhao R, Buyue Q, Xianli Z, Yang L, Rong W, Yang L, Yinggang P (2020) Rethinking dice loss for medical image segmentation. In\u00a02020 IEEE international conference on data mining (ICDM), IEEE, pp. 851\u2013860","DOI":"10.1109\/ICDM50108.2020.00094"},{"issue":"3","key":"10557_CR174","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1111\/j.1467-8640.2010.00358.x","volume":"26","author":"Z-H Zhou","year":"2010","unstructured":"Zhou Z-H, Liu X-Y (2010) On multi-class cost-sensitive learning. Comput Intell 26(3):232\u2013257","journal-title":"Comput Intell"},{"issue":"12","key":"10557_CR175","doi-asserted-by":"crossref","first-page":"4639","DOI":"10.1109\/TCSVT.2019.2962229","volume":"30","author":"JT Zhou","year":"2019","unstructured":"Zhou JT, Zhang L, Fang Z, Jiawei D, Peng X, Xiao Y (2019) Attention-driven loss for anomaly detection in video surveillance. IEEE Trans Circuits Syst Video Technol 30(12):4639\u20134647","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"10557_CR176","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/978-3-319-93040-4_28","volume-title":"Advances in knowledge discovery and data mining","author":"X Zhu","year":"2018","unstructured":"Zhu X, Liu Y, Li J, Wan T, Qin Z (2018) Emotion classification with data augmentation using generative adversarial networks. Advances in knowledge discovery and data mining. Springer International Publishing, Cham, pp 349\u2013360"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10557-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10557-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10557-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T18:41:37Z","timestamp":1729795297000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-023-10557-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,20]]},"references-count":175,"journal-issue":{"issue":"S1","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["10557"],"URL":"https:\/\/doi.org\/10.1007\/s10462-023-10557-6","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,20]]},"assertion":[{"value":"20 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}