{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T16:37:07Z","timestamp":1792255027202,"version":"4.4.0"},"reference-count":96,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"GRRC program of Gyeonggi province","award":["GRRC-Gachon2023(B02)"],"award-info":[{"award-number":["GRRC-Gachon2023(B02)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>The rapid development of abnormal brain cells that characterizes a brain tumor is a major health risk for adults since it can cause severe impairment of organ function and even death. These tumors come in a wide variety of sizes, textures, and locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool. However, detecting brain tumors manually is a difficult and time-consuming activity that might lead to inaccuracies. In order to solve this, we provide a refined You Only Look Once version 7 (YOLOv7) model for the accurate detection of meningioma, glioma, and pituitary gland tumors within an improved detection of brain tumors system. The visual representation of the MRI scans is enhanced by the use of image enhancement methods that apply different filters to the original pictures. To further improve the training of our proposed model, we apply data augmentation techniques to the openly accessible brain tumor dataset. The curated data include a wide variety of cases, such as 2548 images of gliomas, 2658 images of pituitary, 2582 images of meningioma, and 2500 images of non-tumors. We included the Convolutional Block Attention Module (CBAM) attention mechanism into YOLOv7 to further enhance its feature extraction capabilities, allowing for better emphasis on salient regions linked with brain malignancies. To further improve the model\u2019s sensitivity, we have added a Spatial Pyramid Pooling Fast+ (SPPF+) layer to the network\u2019s core infrastructure. YOLOv7 now includes decoupled heads, which allow it to efficiently glean useful insights from a wide variety of data. In addition, a Bi-directional Feature Pyramid Network (BiFPN) is used to speed up multi-scale feature fusion and to better collect features associated with tumors. The outcomes verify the efficiency of our suggested method, which achieves a higher overall accuracy in tumor detection than previous state-of-the-art models. As a result, this framework has a lot of potential as a helpful decision-making tool for experts in the field of diagnosing brain tumors.<\/jats:p>","DOI":"10.3390\/cancers15164172","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T10:28:48Z","timestamp":1692354528000},"page":"4172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":491,"title":["Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5923-8695","authenticated-orcid":false,"given":"Akmalbek Bobomirzaevich","family":"Abdusalomov","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam-si 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1424-0799","authenticated-orcid":false,"given":"Mukhriddin","family":"Mukhiddinov","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam-si 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taeg Keun","family":"Whangbo","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam-si 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"177","DOI":"10.5607\/en.2015.24.3.177","article-title":"Roles of mTOR signaling in brain development","volume":"24","author":"Lee","year":"2015","journal-title":"Exp. Neurobiol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zahoor, M.M., Qureshi, S.A., Bibi, S., Khan, S.H., Khan, A., Ghafoor, U., and Bhutta, M.R. (2022). A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI. Sensors, 22.","DOI":"10.3390\/s22072726"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Arabahmadi, M., Farahbakhsh, R., and Rezazadeh, J. (2022). Deep Learning for Smart healthcare\u2014A Survey on Brain Tumor Detection from Medical Imaging. Sensors, 22.","DOI":"10.3390\/s22051960"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gore, D.V., and Deshpande, V. (2020, January 5\u20137). Comparative study of various techniques using deep Learning for brain tumor detection. Proceedings of the 2020 IEEE International Conference for Emerging Technology (INCET), Belgaum, India.","DOI":"10.1109\/INCET49848.2020.9154030"},{"key":"ref_5","first-page":"2319","article-title":"Brain tumor detection using neural network","volume":"1","author":"Sapra","year":"2013","journal-title":"Int. J. Sci. Mod. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/RBME.2022.3185292","article-title":"Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review","volume":"16","author":"Soomro","year":"2022","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"210","DOI":"10.4103\/ijc.IJC_36_19","article-title":"Factors affecting survival in glioblastoma patients below and above 65 years of age: A retrospective observational study","volume":"58","author":"Yavuz","year":"2021","journal-title":"Indian J. Cancer"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1158\/1055-9965.EPI-20-1443","article-title":"Pediatric brain tumors: Descriptive epidemiology, risk factors, and future directions","volume":"30","author":"Fahmideh","year":"2021","journal-title":"Cancer Epidemiol. Prev. Biomark."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nodirov, J., Abdusalomov, A.B., and Whangbo, T.K. (2022). Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images. Sensors, 22.","DOI":"10.3390\/s22176501"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100608","DOI":"10.1016\/j.imu.2021.100608","article-title":"Classification of brain tumors and auto-immune disease using ensemble learning","volume":"24","author":"Shafi","year":"2021","journal-title":"Inform. Med. Unlocked"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","article-title":"Brain tumor segmentation using convolutional neural networks in MRI images","volume":"35","author":"Pereira","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_12","first-page":"100212","article-title":"Enhanced performance of Dark-Nets for brain tumor classification and segmentation using colormap-based superpixel techniques","volume":"7","author":"Ahuja","year":"2022","journal-title":"Mach. Learn Appl."},{"key":"ref_13","first-page":"106","article-title":"Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment","volume":"Volume 11038","author":"Pereira","year":"2018","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104564","DOI":"10.1016\/j.compbiomed.2021.104564","article-title":"Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification","volume":"135","author":"Tandel","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"249","DOI":"10.2325\/jbcs.13.249","article-title":"Problems in histological grading of malignancy and its clinical significance in patients with operable breast cancer","volume":"13","author":"Komaki","year":"2006","journal-title":"Breast Cancer"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"109696","DOI":"10.1016\/j.mehy.2020.109696","article-title":"Brain tumor classification using modified local binary patterns (LBP) feature extraction methods","volume":"139","author":"Kaplan","year":"2020","journal-title":"Med. Hypotheses"},{"key":"ref_17","first-page":"15","article-title":"Artificial intelligence and patentability: Review and discussions","volume":"1","author":"Chatterjee","year":"2021","journal-title":"Int. J. Mod. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Raut, G., Raut, A., Bhagade, J., Bhagade, J., and Gavhane, S. (2020, January 18\u201320). Deep learning approach for brain tumor detection and segmentation. Proceedings of the 2020 International Conference on Convergence to Digital World\u2014Quo Vadis (ICCDW), Mumbai, India.","DOI":"10.1109\/ICCDW45521.2020.9318681"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mamun, M., Bin Shawkat, S., Ahammed, M.S., Uddin, M.M., Mahmud, M.I., and Islam, A.M. (2022, January 26\u201329). Deep Learning Based Model for Alzheimer\u2019s Disease Detection Using Brain MRI Images. Proceedings of the 2022 IEEE 13th Annual Ubiquitous Computing, Electronics, and Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON54665.2022.9965730"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.patrec.2019.11.020","article-title":"Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019","volume":"131","author":"Tiwari","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tandel, G.S., Biswas, M., Kakde, O.G., Tiwari, A., Suri, H.S., Turk, M., Laird, J.R., Asare, C.K., Ankrah, A.A., and Khanna, N.N. (2019). A review on a deep learning perspective in brain cancer classification. Cancers, 11.","DOI":"10.3390\/cancers11010111"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1038\/s41591-019-0715-9","article-title":"Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks","volume":"26","author":"Hollon","year":"2020","journal-title":"Nature Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9083","DOI":"10.1007\/s11042-019-07785-w","article-title":"Convolutional neural network based early fire detection","volume":"79","author":"Saeed","year":"2020","journal-title":"Multimedia Tools Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"16201","DOI":"10.1007\/s11042-019-7548-x","article-title":"Machine learning based approach for multimedia surveillance during fire emergencies","volume":"79","author":"Saeed","year":"2020","journal-title":"Multimedia Tools Appl."},{"key":"ref_25","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1109\/TNNLS.2020.2995800","article-title":"Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey","volume":"32","author":"Muhammad","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1186\/s13640-018-0332-4","article-title":"Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks","volume":"2018","author":"Awad","year":"2018","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"55135","DOI":"10.1109\/ACCESS.2020.2978629","article-title":"A deep learning model based on concatenation approach for the diagnosis of brain tumor","volume":"8","author":"Noreen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.mri.2019.05.028","article-title":"A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned","volume":"61","author":"Awad","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yushkevich, P.A., Gao, Y., and Gerig, G. (2016, January 16\u201320). ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591443"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polonia, A., and Campilho, A. (2017). Classification of breast cancer histology images using convolutional neural networks. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177544"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"109684","DOI":"10.1016\/j.mehy.2020.109684","article-title":"Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture","volume":"139","author":"Yildirim","year":"2020","journal-title":"Med. Hypotheses"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Somasundaram, S., and Gobinath, R. (2019, January 14\u201316). Current trends on deep learning models for brain tumor segmentation and detection\u2014A review. In Proceedings of the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon 2019), Faridabad, India.","DOI":"10.1109\/COMITCon.2019.8862209"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Latif, G., Brahim, G.B., Iskandar, D.N.F.A., Bashar, A., and Alghazo, J. (2022). Glioma Tumors\u2019 classification using deep-neural-network-based features with SVM classifier. Diagnostics, 12.","DOI":"10.3390\/diagnostics12041018"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kumar, G., Kumar, P., and Kumar, D. (2021, January 3\u20134). Brain tumor detection using convolutional neural network. Proceedings of the International Conference on Mobile Networks and Wireless Communications (ICMNWC), Tumkur, Karnataka, India.","DOI":"10.1109\/ICMNWC52512.2021.9688460"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1002\/jemt.23694","article-title":"Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification","volume":"84","author":"Khan","year":"2021","journal-title":"Microsc. Res. Tech."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yahyaoui, H., Ghazouani, F., and Farah, I.R. (2021, January 4\u20135). Deep learning guided by an ontology for medical images classification using a multimodal fusion. Proceedings of the International Congress of Advanced Technology and Engineering (ICOTEN 2021), Virtual Conference.","DOI":"10.1109\/ICOTEN52080.2021.9493469"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kang, J., Ullah, Z., and Gwak, J. (2021). MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors, 21.","DOI":"10.3390\/s21062222"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s11831-021-09572-z","article-title":"Machine learning application in glioma classification: Review and comparison analysis","volume":"29","author":"Bhatele","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s13534-021-00209-5","article-title":"Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis","volume":"12","author":"Murthy","year":"2022","journal-title":"Biomed. Eng. Lett."},{"key":"ref_43","first-page":"100003","article-title":"A transfer learning approach for AI-based classification of brain tumors","volume":"2","author":"Mehrotra","year":"2020","journal-title":"Mach. Learn. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Grampurohit, S., Shalavadi, V., Dhotargavi, V.R., Kudari, M., and Jolad, S. (2020, January 3\u20134). Brain tumor detection using deep learning models. Proceedings of the 2020 IEEE India Council International Subsections Conference (INDISCON 2020), Visakhapatnam, India.","DOI":"10.1109\/INDISCON50162.2020.00037"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Methil, A.S. (2021, January 25\u201327). Brain tumor detection using deep learning and image processing. Proceedings of the 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Tamil Nadu, India.","DOI":"10.1109\/ICAIS50930.2021.9395823"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.bbe.2018.10.004","article-title":"Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms","volume":"39","author":"Anaraki","year":"2019","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"116942","DOI":"10.1109\/ACCESS.2021.3105874","article-title":"VGG-SCNet: A VGG net-based deep learning framework for brain tumor detection on MRI images","volume":"9","author":"Majib","year":"2021","journal-title":"IEEE Access"},{"key":"ref_48","unstructured":"Siddique, M.A.B., Sakib, S., Khan, M.M.R., Tanzeem, A.K., Chowdhury, M., and Yasmin, N. (2020, January 7\u20139). Deep convolutional neural networks model-based brain tumor detection in brain MRI images. In Proceedings of the 4th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC 2020), SCAD Knowledge City, India."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"180134","DOI":"10.1109\/ACCESS.2019.2958370","article-title":"Brain tumor detection based on multimodal information fusion and convolutional neural network","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Sangeetha, R., Mohanarathinam, A., Aravindh, G., Jayachitra, S., and Bhuvaneswari, M. (2020, January 5\u20137). Automatic detection of brain tumor using deep learning algorithms. Proceedings of the 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA 2020), Coimbatore, India.","DOI":"10.1109\/ICECA49313.2020.9297536"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","article-title":"Deep learning for brain MRI segmentation: State of the art and future directions","volume":"30","author":"Akkus","year":"2017","journal-title":"J. Digit. Imaging"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.jvcir.2018.11.047","article-title":"A mix-pooling CNN architecture with FCRF for brain tumor segmentation","volume":"58","author":"Jie","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_53","unstructured":"Lucas, F., Wenqi, L., Luis, C.G.P.H., Jinendra, E., Neil, K., Sebastian, O., and Tom, V. (2017). Scalable Multimodal Convolutional Networks for Brain Tumour Segmentation Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2017, Springer."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"ZainEldin, H., Gamel, S.A., El-Kenawy, E.-S.M., Alharbi, A.H., Khafaga, D.S., Ibrahim, A., and Talaat, F.M. (2023). Brain Tumor Detection and Classification Using Deep Learning and Sine-Cosine Fitness Grey Wolf Optimization. Bioengineering, 10.","DOI":"10.3390\/bioengineering10010018"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3389\/fncom.2020.00025","article-title":"Brain tumor segmentation using an ensemble of 3d u-nets and overall survival prediction using radiomic features","volume":"14","author":"Feng","year":"2020","journal-title":"Front. Comput. Neurosci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"186","DOI":"10.18383\/j.tom.2019.00026","article-title":"A fully automated deep learning network for brain tumor segmentation","volume":"6","author":"Yogananda","year":"2020","journal-title":"Tomography"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Madhupriya, G., Guru, N.M., Praveen, S., and Nivetha, B. (2019, January 23\u201325). Brain tumor segmentation with deep learning technique. Proceedings of the 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India.","DOI":"10.1109\/ICOEI.2019.8862575"},{"key":"ref_58","unstructured":"Wang, G., Li, W., Ourselin, S., and Vercauteren, T. (2018). Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Third International Workshop, BrainLes 2017, Proceedings of the Conjunction with MICCAI 2017, Quebec City, QC, Canada, 14 September 2017, Springer International Publishing. Revised Selected Papers 3."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/TST.2014.6961028","article-title":"A survey of MRI-based brain tumor segmentation methods","volume":"19","author":"Liu","year":"2014","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","article-title":"Brain tumor segmentation with Deep Neural Networks","volume":"35","author":"Havaei","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_62","first-page":"1112","article-title":"A multi-modal fusion deep learning model for brain tumor grading","volume":"13","author":"Liang","year":"2019","journal-title":"Front. Neurosci."},{"key":"ref_63","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Springer International Publishing. Part V3."},{"key":"ref_64","unstructured":"Sartaj (2023, May 02). Brain Tumor Classification Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/sartajbhuvaji\/brain-tumor-classification-mri."},{"key":"ref_65","unstructured":"Nickparvar, M. (2023, May 02). Brain Tumor Classification Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/masoudnickparvar\/brain-tumor-mri-dataset."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Mukhiddinov, M., Djuraev, O., Akhmedov, F., Mukhamadiyev, A., and Cho, J. (2023). Masked Face Emotion Recognition Based on Facial Landmarks and Deep Learning Approaches for Visually Impaired People. Sensors, 23.","DOI":"10.3390\/s23031080"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Abdusalomov, A.B., Mukhiddinov, M., Kutlimuratov, A., and Whangbo, T.K. (2022). Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People. Sensors, 22.","DOI":"10.3390\/s22197305"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/3446776","article-title":"Understanding deep learning (still) requires rethinking generalization","volume":"64","author":"Zhang","year":"2021","journal-title":"Commun. ACM"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A.A. (2020). Albumentations: Fast and flexible image augmentations. Information, 11.","DOI":"10.3390\/info11020125"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.","DOI":"10.1109\/UV56588.2022.10185474"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 19\u201325). Repvgg: Making vgg-style convnets great again. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"Volume 42","author":"Hu","year":"2020","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional block attention module. Proceedings of the 2018 European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Kim, S.-Y., and Muminov, A. (2023). Forest Fire Smoke Detection Based on Deep Learning Approaches and Unmanned Aerial Vehicle Images. Sensors, 23.","DOI":"10.3390\/s23125702"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_77","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Mukhiddinov, M., Abdusalomov, A.B., and Cho, J. (2022). A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5. Sensors, 22.","DOI":"10.3390\/s22239384"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"65426","DOI":"10.1109\/ACCESS.2022.3184113","article-title":"A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet","volume":"10","author":"Shah","year":"2022","journal-title":"IEEE Access"},{"key":"ref_80","first-page":"4816","article-title":"A Computer-Aided Diagnosis of Brain Tumors Using a Fine-Tuned YOLO-based Model with Transfer Learning","volume":"14","author":"Montalbo","year":"2020","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2022.3176737","article-title":"Znet: Deep learning approach for 2D MRI brain tumor segmentation","volume":"10","author":"Ottom","year":"2022","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2775","DOI":"10.1109\/ACCESS.2022.3140289","article-title":"A new convolutional neural network architecture for automatic detection of brain tumors in magnetic resonance imaging images","volume":"10","author":"Musallam","year":"2022","journal-title":"IEEE Access"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Asad, R., Rehman, S.U., Imran, A., Li, J., Almuhaimeed, A., and Alzahrani, A. (2023). Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach. Biomedicines, 11.","DOI":"10.3390\/biomedicines11010184"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Aleid, A., Alhussaini, K., Alanazi, R., Altwaimi, M., Altwijri, O., and Saad, A.S. (2023). Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images. Appl. Sci., 13.","DOI":"10.3390\/app13063808"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"59099","DOI":"10.1109\/ACCESS.2022.3179376","article-title":"On the performance of deep transfer learning networks for brain tumor detection using MR images","volume":"10","author":"Ahmad","year":"2022","journal-title":"IEEE Access"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.future.2018.08.008","article-title":"Application of multiresolution analysis for automated detection of brain abnormality using MR images: A comparative study","volume":"90","author":"Gudigar","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., and Feng, Q. (2015). Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0140381"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.dsp.2009.07.002","article-title":"Hybrid intelligent techniques for MRI brain images classification","volume":"20","author":"Hosny","year":"2010","journal-title":"Digit. Signal Process."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Masood, M., Nazir, T., Nawaz, M., Mehmood, A., Rashid, J., Kwon, H.Y., Mahmood, T., and Hussain, A. (2021). A novel deep learning method for recognition and classification of brain tumors from MRI images. Diagnostics, 11.","DOI":"10.3390\/diagnostics11050744"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/6695108","article-title":"A systematic approach for MRI brain tumor localization and segmentation using deep learning and active contouring","volume":"2021","author":"Gunasekara","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1006\/jvci.1999.0442","article-title":"Active contours without edges for vector-valued images","volume":"11","author":"Chan","year":"2000","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"D\u00edaz-Pernas, F.J., Mart\u00ednez-Zarzuela, M., Ant\u00f3n-Rodr\u00edguez, M., and Gonz\u00e1lez-Ortega, D. (2021). A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. Healthcare, 9.","DOI":"10.3390\/healthcare9020153"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/978-981-10-9035-6_33","article-title":"Brain tumor classification using convolutional neural network","volume":"Volume 1","author":"Abiwinanda","year":"2019","journal-title":"Proceedings of the World Congress on Medical Physics and Biomedical Engineering 2018"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Ramesh, K.K., Xu, K.M., Trivedi, A.G., Huang, V., Sharghi, V.K., Kleinberg, L.R., Mellon, E.A., Shu, H.-K.G., Shim, H., and Weinberg, B.D. (2023). A Fully Automated Post-Surgical Brain Tumor Segmentation Model for Radiation Treatment Planning and Longitudinal Tracking. Cancers, 15.","DOI":"10.3390\/cancers15153956"},{"key":"ref_95","unstructured":"Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F.C., and Pati, S. (2021). The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1038\/s41597-023-02123-0","article-title":"A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data","volume":"10","author":"Suter","year":"2023","journal-title":"Sci. Data"}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/15\/16\/4172\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:37:07Z","timestamp":1760128627000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/15\/16\/4172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,18]]},"references-count":96,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["cancers15164172"],"URL":"https:\/\/doi.org\/10.3390\/cancers15164172","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,18]]}}}