{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:34:41Z","timestamp":1771065281058,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T00:00:00Z","timestamp":1725494400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T00:00:00Z","timestamp":1725494400000},"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":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01243-2","type":"journal-article","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T20:26:22Z","timestamp":1725567982000},"page":"1184-1211","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Detection of Diabetic Retinopathy Using Discrete Wavelet-Based Center-Symmetric Local Binary Pattern and Statistical Features"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2035-1583","authenticated-orcid":false,"given":"Imtiyaz","family":"Ahmad","sequence":"first","affiliation":[]},{"given":"Vibhav Prakash","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Manoj Madhava","family":"Gore","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,5]]},"reference":[{"key":"1243_CR1","unstructured":"Boyd, Kierstan. American academy of ophthalmology-what is diabetic retinopathy? Available at https:\/\/www.aao.org\/eye-health\/diseases\/what-is-diabetic-retinopathy. Accessed: 2024-03-18."},{"key":"1243_CR2","doi-asserted-by":"crossref","unstructured":"Nikos Tsiknakis, Dimitris Theodoropoulos, Georgios Manikis, Emmanouil Ktistakis, Ourania Boutsora, Alexa Berto, Fabio Scarpa, Alberto Scarpa, Dimitrios\u00a0I Fotiadis, and Kostas Marias. Deep learning for diabetic retinopathy detection and classification based on fundus images: A review. Computers in Biology and Medicine, 135, 2021.","DOI":"10.1016\/j.compbiomed.2021.104599"},{"key":"1243_CR3","unstructured":"Idf diabetes atlas 2021 \u2013 10th edition. Available at https:\/\/diabetesatlas.org\/atlas\/tenth-edition\/. Accessed 2024-03-19."},{"key":"1243_CR4","doi-asserted-by":"crossref","unstructured":"Joanne\u00a0WY Yau, Sophie\u00a0L Rogers, Ryo Kawasaki, Ecosse\u00a0L Lamoureux, Jonathan\u00a0W Kowalski, Toke Bek, Shih-Jen Chen, Jacqueline\u00a0M Dekker, Astrid Fletcher, Jakob Grauslund, et\u00a0al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care, 35(3), 2012.","DOI":"10.2337\/dc11-1909"},{"key":"1243_CR5","doi-asserted-by":"crossref","unstructured":"Rupert\u00a0RA Bourne, Gretchen\u00a0A Stevens, Richard\u00a0A White, Jennifer\u00a0L Smith, Seth\u00a0R Flaxman, Holly Price, Jost\u00a0B Jonas, Jill Keeffe, Janet Leasher, Kovin Naidoo, et\u00a0al. Causes of vision loss worldwide, 1990\u20132010: A systematic analysis. The Lancet Global Health, 1(6), 2013.","DOI":"10.1016\/S2214-109X(13)70113-X"},{"key":"1243_CR6","unstructured":"Kevin\u00a0H Nguyen, Bhupendra\u00a0C Patel, and Prasanna Tadi. Anatomy, head and neck, eye retina. In StatPearls [Internet]. StatPearls Publishing, 2021."},{"key":"1243_CR7","doi-asserted-by":"crossref","unstructured":"Shubhi Gupta, Sanjeev Thakur, and Ashutosh Gupta. Comparative study of different machine learning models for automatic diabetic retinopathy detection using fundus image. Multimedia Tools and Applications, 2023.","DOI":"10.1007\/s11042-023-16813-9"},{"key":"1243_CR8","doi-asserted-by":"crossref","unstructured":"Roy Taylor and Deborah Batey. Handbook of Retinal Screening in Diabetes: Diagnosis and Management. John Wiley & Sons, 2012.","DOI":"10.1002\/9781119968573"},{"key":"1243_CR9","doi-asserted-by":"crossref","unstructured":"Dolly Das, Saroj\u00a0Kr Biswas, and Sivaji Bandyopadhyay. A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning. Multimedia Tools and Applications, 2022.","DOI":"10.1007\/s11042-022-12642-4"},{"key":"1243_CR10","doi-asserted-by":"crossref","unstructured":"P\u00a0Saranya, R\u00a0Pranati, and Sneha\u00a0Shruti Patro. Detection and classification of red lesions from retinal images for diabetic retinopathy detection using deep learning models. Multimedia Tools and Applications, 2023.","DOI":"10.1007\/s11042-023-15045-1"},{"key":"1243_CR11","doi-asserted-by":"crossref","unstructured":"Abd\u00fcssamed Erciyas and Necaattin Bar\u0131\u015f\u00e7\u0131. An effective method for detecting and classifying diabetic retinopathy lesions based on deep learning. Computational and Mathematical Methods in Medicine, 2021, 2021.","DOI":"10.1155\/2021\/9928899"},{"key":"1243_CR12","doi-asserted-by":"crossref","unstructured":"Early Treatment Diabetic Retinopathy Study\u00a0Research Group et\u00a0al. Grading diabetic retinopathy from stereoscopic color fundus photographs\u2014an extension of the modified airlie house classification: Etdrs report number 10. Ophthalmology, 127(4), 2020.","DOI":"10.1016\/j.ophtha.2020.01.030"},{"key":"1243_CR13","doi-asserted-by":"crossref","unstructured":"Imtiyaz Ahmad, Vibhav\u00a0Prakash Singh, and Suneeta Agarwal. Detection of diabetic retinopathy using deep learning-based framework. In Machine Intelligence and Smart Systems: Proceedings of MISS 2021. Springer, 2022.","DOI":"10.1007\/978-981-16-9650-3_17"},{"key":"1243_CR14","unstructured":"Manal Alsuwat, Hana Alalawi, Shema Alhazmi, and Sarah Al-Shareef. Prediction of diabetic retinopathy using convolutional neural networks."},{"key":"1243_CR15","doi-asserted-by":"crossref","unstructured":"Angel Ayala, Tom\u00e1s Ortiz\u00a0Figueroa, Bruno Fernandes, and Francisco Cruz. Diabetic retinopathy improved detection using deep learning. Applied Sciences, 11(24), 2021.","DOI":"10.3390\/app112411970"},{"key":"1243_CR16","doi-asserted-by":"crossref","unstructured":"Natasha Shaukat, Javeria Amin, Muhammad Sharif, Faisal Azam, Seifedine Kadry, and Sujatha Krishnamoorthy. Three-dimensional semantic segmentation of diabetic retinopathy lesions and grading using transfer learning. Journal of Personalized Medicine, 12(9), 2022.","DOI":"10.3390\/jpm12091454"},{"key":"1243_CR17","doi-asserted-by":"crossref","unstructured":"Muthu Rama\u00a0Krishnan Mookiah, U\u00a0Rajendra Acharya, Chua\u00a0Kuang Chua, Choo\u00a0Min Lim, EYK Ng, and Augustinus Laude. Computer-aided diagnosis of diabetic retinopathy: A review. Computers in biology and medicine, 43(12), 2013.","DOI":"10.1016\/j.compbiomed.2013.10.007"},{"key":"1243_CR18","doi-asserted-by":"crossref","unstructured":"Sima Sahu, Amit\u00a0Kumar Singh, SP\u00a0Ghrera, Mohamed Elhoseny, et\u00a0al. An approach for de-noising and contrast enhancement of retinal fundus image using clahe. Optics & Laser Technology, 110, 2019.","DOI":"10.1016\/j.optlastec.2018.06.061"},{"key":"1243_CR19","doi-asserted-by":"crossref","unstructured":"Imtiyaz Ahmad, Vibhav\u00a0Prakash Singh, and Manoj\u00a0Madhava Gore. Diabetic retinopathy detection based on lbp and statistical features using machine learning. In 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3), 2023.","DOI":"10.1109\/IC2E357697.2023.10262780"},{"key":"1243_CR20","doi-asserted-by":"crossref","unstructured":"A\u00a0Reethika, J\u00a0Sathish, P\u00a0Kanaga Priya, Finney\u00a0Daniel Shadrach, and MS\u00a0Kanivarshini. Diabetic retinopathy detection using statistical features. In 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), volume\u00a02. IEEE, 2022.","DOI":"10.1109\/ICIPTM54933.2022.9753932"},{"key":"1243_CR21","unstructured":"Jorge De\u00a0la Calleja, Lourdes Tecuapetla, Ma\u00a0Auxilio\u00a0Medina, Everardo B\u00e1rcenas, and Argelia\u00a0B Urbina\u00a0N\u00e1jera. Lbp and machine learning for diabetic retinopathy detection. In Intelligent Data Engineering and Automated Learning\u2013IDEAL 2014: 15th International Conference, Salamanca, Spain, September 10-12, 2014. Proceedings 15. Springer, 2014."},{"issue":"3","key":"1243_CR22","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/j.patcog.2008.08.014","volume":"42","author":"Marko Heikkil\u00e4","year":"2009","unstructured":"Marko Heikkil\u00e4, Matti Pietik\u00e4inen, and Cordelia Schmid. Description of interest regions with local binary patterns. Pattern recognition, 42(3):425\u2013436, 2009.","journal-title":"Pattern recognition"},{"key":"1243_CR23","doi-asserted-by":"crossref","unstructured":"Stephane\u00a0G Mallat. A theory for multiresolution signal decomposition: the wavelet representation. IEEE transactions on pattern analysis and machine intelligence, 11(7), 1989.","DOI":"10.1109\/34.192463"},{"key":"1243_CR24","doi-asserted-by":"crossref","unstructured":"Chaymaa Lahmar and Ali Idri. Classifying diabetic retinopathy using cnn and machine learning. In Bioimaging, 2022a.","DOI":"10.5220\/0010851500003123"},{"key":"1243_CR25","doi-asserted-by":"crossref","unstructured":"Mohamed\u00a0A Berbar. Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy. Health Information Science and Systems, 10(1), 2022.","DOI":"10.1007\/s13755-022-00181-z"},{"key":"1243_CR26","doi-asserted-by":"crossref","unstructured":"Guanghui Yue, Yuan Li, Tianwei Zhou, Xiaoyan Zhou, Yun Liu, and Tianfu Wang. Attention-driven cascaded network for diabetic retinopathy grading from fundus images. Biomedical Signal Processing and Control, 80, 2023.","DOI":"10.1016\/j.bspc.2022.104370"},{"key":"1243_CR27","doi-asserted-by":"crossref","unstructured":"S\u00a0Steffi and WR\u00a0Sam Emmanuel. Automated microaneurysms detection in retinal images using ssa optimised u-net and bayesian optimised cnn. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023.","DOI":"10.1080\/21681163.2023.2244603"},{"key":"1243_CR28","unstructured":"Ghulam Ali, Aqsa Dastgir, Muhammad\u00a0Waseem Iqbal, Muhammad Anwar, and Muhammad Faheem. A hybrid convolutional neural network model for automatic diabetic retinopathy classification from fundus images. IEEE Journal of Translational Engineering in Health and Medicine, 2023."},{"key":"1243_CR29","doi-asserted-by":"crossref","unstructured":"Murat Canayaz. Classification of diabetic retinopathy with feature selection over deep features using nature-inspired wrapper methods. Applied Soft Computing, 128, 2022.","DOI":"10.1016\/j.asoc.2022.109462"},{"key":"1243_CR30","doi-asserted-by":"crossref","unstructured":"Thiago\u00a0Fernandes de\u00a0Sousa and Celso\u00a0Gon\u00e7alves Camilo. Hdeep: Hierarchical deep learning combination for detection of diabetic retinopathy. Procedia Computer Science, 222, 2023.","DOI":"10.1016\/j.procs.2023.08.181"},{"key":"1243_CR31","doi-asserted-by":"crossref","unstructured":"G\u00a0Shraddha, S\u00a0Srikrishna, Prajoona Valsalan, Sudiksha Bhat, and P\u00a0Jisha. Identification of suitable machine learning or deep learning algorithmfor diabetic retinopathy detection. In 2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES). IEEE, 2023.","DOI":"10.1109\/ICSSES58299.2023.10200453"},{"key":"1243_CR32","unstructured":"Misgina\u00a0Tsighe Hagos and Shri Kant. Transfer learning based detection of diabetic retinopathy from small dataset. arXiv:1905.07203, 2019."},{"key":"1243_CR33","doi-asserted-by":"crossref","unstructured":"V\u00edctor Vives-Boix and Daniel Ruiz-Fern\u00e1ndez. Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity. Computer Methods and Programs in Biomedicine, 206, 2021.","DOI":"10.1016\/j.cmpb.2021.106094"},{"key":"1243_CR34","doi-asserted-by":"crossref","unstructured":"Chaymaa Lahmar and Ali Idri. On the value of deep learning for diagnosing diabetic retinopathy. Health and Technology, 2022b.","DOI":"10.1007\/s12553-021-00606-x"},{"key":"1243_CR35","unstructured":"Poonam\u00a0M Rokade and Ramesh\u00a0R Manza. Automatic detection of hard exudates in retinal images using haar wavelet transform. Eye, 4(5), 2015."},{"key":"1243_CR36","doi-asserted-by":"crossref","unstructured":"Nagur\u00a0Shareef Shaik and Teja\u00a0Krishna Cherukuri. Lesion-aware attention with neural support vector machine for retinopathy diagnosis. Machine Vision and Applications, 32(6), 2021.","DOI":"10.1007\/s00138-021-01253-y"},{"key":"1243_CR37","unstructured":"Saket\u00a0S Chaturvedi, Kajol Gupta, Vaishali Ninawe, and Prakash\u00a0S Prasad. Automated diabetic retinopathy grading using deep convolutional neural network. arXiv:2004.06334, 2020."},{"key":"1243_CR38","doi-asserted-by":"crossref","unstructured":"Jyostna\u00a0Devi Bodapati, Veeranjaneyulu Naralasetti, Shaik\u00a0Nagur Shareef, Saqib Hakak, Muhammad Bilal, Praveen Kumar\u00a0Reddy Maddikunta, and Ohyun Jo. Blended multi-modal deep convnet features for diabetic retinopathy severity prediction. Electronics, 9(6), 2020.","DOI":"10.3390\/electronics9060914"},{"key":"1243_CR39","doi-asserted-by":"crossref","unstructured":"Israa\u00a0Y Abushawish, Sudipta Modak, Esam Abdel-Raheem, Soliman\u00a0A Mahmoud, and Abir\u00a0Jaafar Hussain. Deep learning in automatic diabetic retinopathy detection and grading systems: A comprehensive survey and comparison of methods. IEEE Access, 12, 2024.","DOI":"10.1109\/ACCESS.2024.3415617"},{"key":"1243_CR40","doi-asserted-by":"crossref","unstructured":"Venkatesan Rajinikanth, Seifedine Kadry, Robertas Dama\u0161evi\u010dius, David Taniar, and Hafiz\u00a0Tayyab Rauf. Machine-learning-scheme to detect choroidal-neovascularization in retinal oct image. In 2021 seventh international conference on bio signals, images, and instrumentation (ICBSII). IEEE, 2021.","DOI":"10.1109\/ICBSII51839.2021.9445134"},{"key":"1243_CR41","doi-asserted-by":"crossref","unstructured":"Lakshmana\u00a0Kumar Ramasamy, Shynu\u00a0Gopalan Padinjappurathu, Seifedine Kadry, and Robertas Dama\u0161evi\u010dius. Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier. PeerJ computer science, 7, 2021.","DOI":"10.7717\/peerj-cs.456"},{"key":"1243_CR42","doi-asserted-by":"crossref","unstructured":"Sarmad Maqsood, Robertas Dama\u0161evi\u010dius, and Rytis Maskeli\u016bnas. Hemorrhage detection based on 3d cnn deep learning framework and feature fusion for evaluating retinal abnormality in diabetic patients. Sensors, 21(11), 2021a.","DOI":"10.3390\/s21113865"},{"key":"1243_CR43","doi-asserted-by":"crossref","unstructured":"Sarmad Maqsood, Robertas Dama\u0161evi\u010dius, Faisal\u00a0Mehmood Shah, and Rytis Maskeliunas. Detection of macula and recognition of aged-related macular degeneration in retinal fundus images. Computing and Informatics, 40(5), 2021b.","DOI":"10.31577\/cai_2021_5_957"},{"key":"1243_CR44","doi-asserted-by":"crossref","unstructured":"Seifedine Kadry, Venkatesan Rajinikanth, Robertas Dama\u0161evi\u010dius, and David Taniar. Retinal vessel segmentation with slime-mould-optimization based multi-scale-matched-filter. In 2021 seventh international conference on bio signals, images, and instrumentation (ICBSII). IEEE, 2021.","DOI":"10.1109\/ICBSII51839.2021.9445135"},{"key":"1243_CR45","doi-asserted-by":"crossref","unstructured":"Vanessa Buhrmester, David M\u00fcnch, and Michael Arens. Analysis of explainers of black box deep neural networks for computer vision: A survey. Machine Learning and Knowledge Extraction, 3(4), 2021.","DOI":"10.3390\/make3040048"},{"key":"1243_CR46","doi-asserted-by":"crossref","unstructured":"Vikas Hassija, Vinay Chamola, Atmesh Mahapatra, Abhinandan Singal, Divyansh Goel, Kaizhu Huang, Simone Scardapane, Indro Spinelli, Mufti Mahmud, and Amir Hussain. Interpreting black-box models: a review on explainable artificial intelligence. Cognitive Computation, 16(1), 2024.","DOI":"10.1007\/s12559-023-10179-8"},{"key":"1243_CR47","doi-asserted-by":"crossref","unstructured":"Imtiyaz Ahmad, Vibhav\u00a0Prakash Singh, and Manoj\u00a0Madhava Gore. Segmented fractal and central symmetric lbp based texture features for the detection of diabetic retinopathy using svm. SN Computer Science, 5(5), 2024.","DOI":"10.1007\/s42979-024-02996-x"},{"key":"1243_CR48","doi-asserted-by":"crossref","unstructured":"Chunlei Chen, Peng Zhang, Huixiang Zhang, Jiangyan Dai, Yugen Yi, Huihui Zhang, and Yonghui Zhang. Deep learning on computational-resource-limited platforms: a survey. Mobile Information Systems, 2020, 2020.","DOI":"10.1155\/2020\/8454327"},{"key":"1243_CR49","doi-asserted-by":"crossref","unstructured":"V\u00a0Radhamani, V\u00a0Venkataramanan, S\u00a0Diwakaran, Muthukumar Subramanian, and Arun\u00a0Sekar Rajasekaran. Wavelet thresholding techniques implementation in retinal images for suppressing noises. Materials Today: Proceedings, 57, 2022.","DOI":"10.1016\/j.matpr.2021.12.059"},{"key":"1243_CR50","unstructured":"K\u00a0Malathi and R\u00a0Nedunchelian. Comparison of various noises and filters for fundus images using pre-processing techniques. International Journal of Pharma and Bio Sciences, 5 (3), 2014."},{"key":"1243_CR51","doi-asserted-by":"crossref","unstructured":"M\u00a0Usman Akram, Shehzad Khalid, and Shoab\u00a0A Khan. Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern recognition, 46(1), 2013.","DOI":"10.1016\/j.patcog.2012.07.002"},{"key":"1243_CR52","doi-asserted-by":"crossref","unstructured":"PN\u00a0Sharath Kumar, R\u00a0Rajesh Kumar, Anuja Sathar, and V\u00a0Sahasranamam. Automatic detection of exudates in retinal images using histogram analysis. In 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS). IEEE, 2013.","DOI":"10.1109\/RAICS.2013.6745487"},{"key":"1243_CR53","doi-asserted-by":"crossref","unstructured":"Vibhav\u00a0Prakash Singh and Rajeev Srivastava. Automated and effective content-based mammogram retrieval using wavelet based cs-lbp feature and self-organizing map. Biocybernetics and Biomedical Engineering, 38(1), 2018.","DOI":"10.1016\/j.bbe.2017.09.003"},{"key":"1243_CR54","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1007\/s11517-007-0273-z","volume":"45","author":"April Khademi","year":"2007","unstructured":"April Khademi and Sridhar Krishnan. Shift-invariant discrete wavelet transform analysis for retinal image classification. Medical & biological engineering & computing, 45: 1211\u20131222, 2007.","journal-title":"Medical & biological engineering & computing"},{"key":"1243_CR55","doi-asserted-by":"crossref","unstructured":"Jianguo Xu, Weihua Yang, Cheng Wan, and Jianxin Shen. Weakly supervised detection of central serous chorioretinopathy based on local binary patterns and discrete wavelet transform. Computers in Biology and Medicine, 127, 2020.","DOI":"10.1016\/j.compbiomed.2020.104056"},{"key":"1243_CR56","doi-asserted-by":"crossref","unstructured":"Kevin Noronha, U\u00a0Rajendra Acharya, KP\u00a0Nayak, S\u00a0Kamath, and Sulatha\u00a0V Bhandary. Decision support system for diabetic retinopathy using discrete wavelet transform. Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine, 227(3), 2013.","DOI":"10.1177\/0954411912470240"},{"key":"1243_CR57","unstructured":"KM\u00a0Alabdulwahhab, W\u00a0Sami, T\u00a0Mehmood, SA\u00a0Meo, TA\u00a0Alasbali, and FA\u00a0Alwadani. Automated detection of diabetic retinopathy using machine learning classifiers. Eur Rev Med Pharmacol Sci, 25(2), 2021."},{"key":"1243_CR58","doi-asserted-by":"crossref","unstructured":"Jorma Laaksonen and Erkki Oja. Classification with learning k-nearest neighbors. In Proceedings of International Conference on Neural Networks (ICNN\u201996), volume\u00a03. IEEE, 1996.","DOI":"10.1109\/ICNN.1996.549118"},{"key":"1243_CR59","doi-asserted-by":"crossref","unstructured":"Zhongheng Zhang. Introduction to machine learning: K-nearest neighbors. Annals of Translational Medicine, 4(11), 2016.","DOI":"10.21037\/atm.2016.03.37"},{"key":"1243_CR60","unstructured":"Aravind eye hospital, aptos 2019 blindness detection. Online available at https:\/\/www.kaggle.com\/c\/aptos2019-blindness-detection."},{"key":"1243_CR61","doi-asserted-by":"crossref","unstructured":"Max\u00a0A Little, Gael Varoquaux, Sohrab Saeb, Luca Lonini, Arun Jayaraman, David\u00a0C Mohr, and Konrad\u00a0P Kording. Using and understanding cross-validation strategies. perspectives on saeb et\u00a0al. GigaScience, 6(5), 2017.","DOI":"10.1093\/gigascience\/gix020"},{"key":"1243_CR62","doi-asserted-by":"crossref","unstructured":"Davoud Gholamiangonabadi, Nikita Kiselov, and Katarina Grolinger. Deep neural networks for human activity recognition with wearable sensors: Leave-one-subject-out cross-validation for model selection. Ieee Access, 8, 2020.","DOI":"10.1109\/ACCESS.2020.3010715"},{"key":"1243_CR63","doi-asserted-by":"crossref","unstructured":"Prasanna Porwal, Samiksha Pachade, Ravi Kamble, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe, and Fabrice Meriaudeau. Indian diabetic retinopathy image dataset (idrid): a database for diabetic retinopathy screening research. Data, 3(3), 2018.","DOI":"10.3390\/data3030025"},{"key":"1243_CR64","doi-asserted-by":"crossref","unstructured":"Etienne Decenci\u00e8re, Xiwei Zhang, Guy Cazuguel, Bruno Lay, B\u00e9atrice Cochener, Caroline Trone, Philippe Gain, Richard Ordonez, Pascale Massin, Ali Erginay, et\u00a0al. Feedback on a publicly distributed image database: the messidor database. Image Analysis and Stereology, 33(3), 2014.","DOI":"10.5566\/ias.1155"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01243-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01243-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01243-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T14:16:28Z","timestamp":1743344188000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01243-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,5]]},"references-count":64,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["1243"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01243-2","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,5]]},"assertion":[{"value":"4 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2024","order":4,"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 that this article does not include any research involving human participants or animals.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}