{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T14:45:45Z","timestamp":1793457945473,"version":"4.5.0"},"reference-count":71,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p><jats:bold>Introduction<\/jats:bold>:<\/jats:p>\n<jats:p>Retinal diseases, a significant global health concern, often lead to severe vision impairment and blindness, resulting in substantial functional and social limitations. This study explored a novel goal of developing and comparing the performance of multiple state-of-the-art convolutional neural network (CNN) models for the automated detection and classification of retinal diseases using optical coherence tomography (OCT) images.<\/jats:p>\n<jats:p><jats:bold>Method<\/jats:bold>:<\/jats:p>\n<jats:p>The study utilized several models, including DenseNet121, ResNet50, Inception V3, MobileNet, and OCT images obtained from the WATBORG Eye Clinic, to detect and classify multiple retinal diseases such as glaucoma, macular edema, posterior vitreous detachment (PVD), and normal eye cases. The preprocessing techniques employed included data augmentation, resizing, and one-hot encoding. We also used the Gaussian Process-based Bayesian Optimization (GPBBO) approach to fine-tune the hyperparameters. Model performance was evaluated using the F1-Score, precision, recall, and area under the curve (AUC).<\/jats:p>\n<jats:p><jats:bold>Result<\/jats:bold>:<\/jats:p>\n<jats:p>All the CNN models evaluated in this study demonstrated a strong capability to detect and classify various retinal diseases with high accuracy. MobileNet achieved the highest accuracy at 96% and AUC of 0.975, closely followed by DenseNet121, which had 95% accuracy and an AUC of 0.963. Inception V3 and ResNet50, while not as high in accuracy, showed potential in specific contexts, with 83% and 79% accuracy, respectively.<\/jats:p>\n<jats:p><jats:bold>Conclusion<\/jats:bold>:<\/jats:p>\n<jats:p>These results underscore the potential of advanced CNN models for diagnosing retinal diseases. With the exception of ResNet50, the other CNN models displayed accuracy levels that are comparable to other state-of-the-art deep learning models. Notably, MobileNet and DenseNet121 showed considerable promise for use in clinical settings, enabling healthcare practitioners to make rapid and accurate diagnoses of retinal diseases. Future research should focus on expanding datasets, integrating multi-modal data, exploring hybrid models, and validating these models in clinical environments to further enhance their performance and real-world applicability.<\/jats:p>","DOI":"10.1371\/journal.pone.0327743","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T17:32:59Z","timestamp":1754069579000},"page":"e0327743","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["A comparative study of machine learning models for automated detection and classification of retinal diseases in Ghana"],"prefix":"10.1371","volume":"20","author":[{"given":"Gifty","family":"Duah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1666-7489","authenticated-orcid":true,"given":"Eric","family":"Nyarko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7776-6501","authenticated-orcid":true,"given":"Anani","family":"Lotsi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"issue":"12","key":"pone.0327743.ref001","doi-asserted-by":"crossref","DOI":"10.1016\/S2214-109X(17)30393-5","article-title":"Global causes of blindness and distance vision impairment 1990 -2020: a systematic review and meta-analysis","volume":"5","author":"SR Flaxman","year":"2017","journal-title":"Lancet Glob Health."},{"key":"pone.0327743.ref002","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1186\/s12886-015-0175-z","article-title":"Population awareness of diabetic eye disease and age related macular degeneration in Nepal: the Bhaktapur Retina Study","volume":"15","author":"R Thapa","year":"2015","journal-title":"BMC Ophthalmol."},{"key":"pone.0327743.ref003","doi-asserted-by":"crossref","first-page":"04126","DOI":"10.7189\/jogh.13.04126","article-title":"Visual impairment and blindness caused by retinal diseases: a nationwide register-based study","volume":"13","author":"C Zhou","year":"2023","journal-title":"J Glob Health."},{"key":"pone.0327743.ref004","doi-asserted-by":"crossref","first-page":"100261","DOI":"10.1016\/j.health.2023.100261","article-title":"A systematic review of retinal fundus image segmentation and classification methods using convolutional neural networks","volume":"4","author":"AE Ilesanmi","year":"2023","journal-title":"Healthc Analyt."},{"issue":"1","key":"pone.0327743.ref005","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1186\/s12875-024-02406-4","article-title":"Sustainable solutions to barriers of point-of-care diagnostic testing services in health facilities without laboratories in the bono region, Ghana: a qualitative study","volume":"25","author":"M Ansu-Mensah","year":"2024","journal-title":"BMC Prim Care."},{"issue":"3","key":"pone.0327743.ref006","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1097\/APO.0000000000000525","article-title":"Patients perceptions of artificial intelligence in diabetic eye screening","volume":"11","author":"A Yap","year":"2022","journal-title":"Asia Pac J Ophthalmol (Phila)."},{"issue":"2","key":"pone.0327743.ref007","first-page":"57","article-title":"Enhancing retinal disease diagnosis through AI: evaluating performance, ethical considerations, and clinical implementation","volume":"1","author":"M Fatima","year":"2024","journal-title":"Inf Health."},{"key":"pone.0327743.ref008","doi-asserted-by":"crossref","unstructured":"Sertkaya ME, Ergen B, Togacar M. Diagnosis of eye retinal diseases based on convolutional neural networks using optical coherence images. In: 2019 23rd International Conference Electronics. 2019. p. 1\u20135.","DOI":"10.1109\/ELECTRONICS.2019.8765579"},{"key":"pone.0327743.ref009","doi-asserted-by":"crossref","unstructured":"Prawira R, Bustamam A, Anki P. Multi label classification of retinal disease on fundus images using AlexNet and VGG16 architectures. In: 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). 2021. p. 464\u20138. https:\/\/doi.org\/10.1109\/isriti54043.2021.9702817","DOI":"10.1109\/ISRITI54043.2021.9702817"},{"issue":"17","key":"pone.0327743.ref010","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e37293","article-title":"MobileNet-V2 \/IFHO model for accurate detection of early-stage diabetic retinopathy","volume":"10","author":"C Huang","year":"2024","journal-title":"Heliyon."},{"issue":"10","key":"pone.0327743.ref011","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.3390\/diagnostics13101738","article-title":"Automatic diagnosis of glaucoma from retinal images using deep learning approach","volume":"13","author":"A Shoukat","year":"2023","journal-title":"Diagnostics (Basel)."},{"issue":"11","key":"pone.0327743.ref012","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0288663","article-title":"CLAHE-CapsNet: efficient retina optical coherence tomography classification using capsule networks with contrast limited adaptive histogram equalization","volume":"18","author":"M Opoku","year":"2023","journal-title":"PLoS One."},{"issue":"1","key":"pone.0327743.ref013","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1186\/s12913-021-06594-y","article-title":"Utilization of eye health services and diabetic retinopathy: a cross-sectional study among persons living with diabetes visiting a tertiary eye care facility in Ghana","volume":"21","author":"B Akrofi","year":"2021","journal-title":"BMC Health Serv Res."},{"issue":"1","key":"pone.0327743.ref014","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1186\/s12913-021-06608-9","article-title":"Situational analysis of diabetic retinopathy treatment services in Ghana","volume":"21","author":"A Mensah-Debrah","year":"2021","journal-title":"BMC Health Serv Res."},{"key":"pone.0327743.ref015","unstructured":"Adjei PE. A new image processing algorithm for computer aided prediction of glaucoma in Ghana. 2016."},{"key":"pone.0327743.ref016","unstructured":"World Health Organization. Report of the 2030 targets on effective coverage of eye care. World Health Organization; 2022."},{"key":"pone.0327743.ref017","doi-asserted-by":"crossref","unstructured":"Shyamalee T, Meedeniya D. Attention U-Net for glaucoma identification using fundus image segmentation. In: 2022 International Conference on Decision Aid Sciences and Applications (DASA). 2022. p. 6\u201310.","DOI":"10.1109\/DASA54658.2022.9765303"},{"issue":"3","key":"pone.0327743.ref018","doi-asserted-by":"crossref","first-page":"240","DOI":"10.3390\/bioengineering11030240","article-title":"Advancing ocular imaging: a hybrid attention mechanism-based U-Net model for precise segmentation of sub-retinal layers in OCT images","volume":"11","author":"PK Karn","year":"2024","journal-title":"Bioengineering (Basel)."},{"issue":"6","key":"pone.0327743.ref019","first-page":"1186","article-title":"Deep learning for glaucoma detection: R-cnn resnet-50 and image segmentation","volume":"14","author":"MS Puchaicela-Lozano","year":"2023","journal-title":"J Adv Inf Technol."},{"issue":"7","key":"pone.0327743.ref020","doi-asserted-by":"crossref","first-page":"823","DOI":"10.3390\/bioengineering10070823","article-title":"Multi-stage classification of retinal OCT using multi-scale ensemble deep architecture","volume":"10","author":"O Akinniyi","year":"2023","journal-title":"Bioengineering (Basel)."},{"issue":"1","key":"pone.0327743.ref021","doi-asserted-by":"crossref","DOI":"10.1136\/bmjophth-2021-000924","article-title":"Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs","volume":"7","author":"E Abitbol","year":"2022","journal-title":"BMJ Open Ophthalmol."},{"key":"pone.0327743.ref022","doi-asserted-by":"crossref","unstructured":"Arora A, Gupta S, Singh S, Dubey J. Eye disease detection using transfer learning on vgg16. In: Proceedings of Third International Conference on Computing, Communications and Cyber-Security: IC4S 2021 . 2022. p. 527\u201336.","DOI":"10.1007\/978-981-19-1142-2_42"},{"issue":"7","key":"pone.0327743.ref023","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0307317","article-title":"A deep learning framework for the early detection of multi-retinal diseases","volume":"19","author":"S Ejaz","year":"2024","journal-title":"PLoS One."},{"issue":"5","key":"pone.0327743.ref024","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.3390\/s22052016","article-title":"OCT retinal and choroidal layer instance segmentation using mask R-CNN","volume":"22","author":"IA Viedma","year":"2022","journal-title":"Sensors."},{"key":"pone.0327743.ref025","doi-asserted-by":"crossref","unstructured":"Bilal A, Sun G, Mazhar S, Imran A. Improved grey wolf optimization-based feature selection, classification using CNN for diabetic retinopathy detection. Evolutionary computing and mobile sustainable networks: proceedings of ICECMSN 2021 . Springer; 2022. p. 1\u201314.","DOI":"10.1007\/978-981-16-9605-3_1"},{"issue":"14","key":"pone.0327743.ref026","doi-asserted-by":"crossref","first-page":"4916","DOI":"10.3390\/app10144916","article-title":"Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images","volume":"10","author":"S Sreng","year":"2020","journal-title":"Appl Sci."},{"key":"pone.0327743.ref027","doi-asserted-by":"crossref","first-page":"121015","DOI":"10.1016\/j.ins.2024.121015","article-title":"A retinal vessel segmentation network approach based on rough sets and attention fusion module","volume":"678","author":"Z Gao","year":"2024","journal-title":"Inf Sci."},{"key":"pone.0327743.ref028","article-title":"A novel centralized federated deep fuzzy neural network with multi-objectives neural architecture search for epistatic detection","author":"X Wu","year":"2024","journal-title":"IEEE Trans Fuzzy Syst."},{"key":"pone.0327743.ref029","article-title":"A review of pulse-coupled neural network applications in computer vision and image processing","author":"N Rafi","year":"2024","journal-title":"arXiv preprint"},{"key":"pone.0327743.ref030","article-title":"Practical bayesian optimization of machine learning algorithms","volume":"25","author":"J Snoek","year":"2012","journal-title":"Adv Neural Inf Process Syst."},{"key":"pone.0327743.ref031","author":"R Jain","year":"1995","journal-title":"Machine vision."},{"key":"pone.0327743.ref032","unstructured":"Sonka M, Hlavac V, Boyle R. Image processing, analysis and machine vision. Springer; 2013."},{"issue":"21","key":"pone.0327743.ref033","doi-asserted-by":"crossref","first-page":"8741","DOI":"10.3390\/s23218741","article-title":"Detection of drowsiness among drivers using novel deep convolutional neural network model","volume":"23","author":"F Majeed","year":"2023","journal-title":"Sensors (Basel)."},{"issue":"5","key":"pone.0327743.ref034","doi-asserted-by":"crossref","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"DS Kermany","year":"2018","journal-title":"Cell."},{"key":"pone.0327743.ref035","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.compbiomed.2017.01.018","article-title":"Comparing humans and deep learning performance for grading AMD: a study in using universal deep features and transfer learning for automated AMD analysis","volume":"82","author":"P Burlina","year":"2017","journal-title":"Comput Biol Med."},{"issue":"7","key":"pone.0327743.ref036","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.3390\/diagnostics12071607","article-title":"Diabetic retinopathy detection from fundus images of the eye using hybrid deep learning features","volume":"12","author":"MM Butt","year":"2022","journal-title":"Diagnostics (Basel)."},{"key":"pone.0327743.ref037","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.compbiomed.2018.06.007","article-title":"System-on-chip design of the cortical-diencephalic centre of the lower urinary tract","volume":"99","author":"FM P\u00e9rez","year":"2018","journal-title":"Comput Biol Med."},{"issue":"7","key":"pone.0327743.ref038","doi-asserted-by":"crossref","first-page":"690","DOI":"10.3390\/jpm14070690","article-title":"Novel approaches for early detection of retinal diseases using artificial intelligence","volume":"14","author":"FS Sorrentino","year":"2024","journal-title":"J Pers Med."},{"key":"pone.0327743.ref039","doi-asserted-by":"crossref","first-page":"100216","DOI":"10.1016\/j.health.2023.100216","article-title":"A comprehensive review of deep neural networks for medical image processing: recent developments and future opportunities","volume":"4","author":"PK Mall","year":"2023","journal-title":"Healthc Analyt."},{"issue":"2","key":"pone.0327743.ref040","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: a technical tutorial on the state of the art","volume":"4","author":"L Zhang","year":"2016","journal-title":"IEEE Geosci Remote Sens Magaz."},{"issue":"42","key":"pone.0327743.ref041","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.17485\/IJST\/v15i42.1812","article-title":"Medical image fusion using CNN with automated pooling","volume":"15","author":"GJ Trivedi","year":"2022","journal-title":"IJST."},{"key":"pone.0327743.ref042","unstructured":"Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA. 2011. p. 315\u201323."},{"issue":"2","key":"pone.0327743.ref043","article-title":"A hybrid model for ischemic stroke brain segmentation from MRI images using CBAM and ResNet50-UNet","volume":"15","author":"F ABOUDI","year":"2024","journal-title":"Int J Adv Comput Sci Appl."},{"issue":"3","key":"pone.0327743.ref044","article-title":"Intelligent deep convolutional neural network based object detection model for visually challenged people","volume":"46","author":"SK Devi","year":"2023","journal-title":"Comput Syst Sci Eng."},{"issue":"12","key":"pone.0327743.ref045","doi-asserted-by":"crossref","first-page":"8704","DOI":"10.1109\/TPAMI.2019.2918284","article-title":"Convolutional networks with dense connectivity","volume":"44","author":"G Huang","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"pone.0327743.ref046","article-title":"Mobilenets: efficient convolutional neural networks for mobile vision applications","author":"AG Howard","year":"2017","journal-title":"arXiv preprint"},{"issue":"4","key":"pone.0327743.ref047","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.3390\/stats5040064","article-title":"Bias-corrected maximum likelihood estimation and Bayesian inference for the process performance index using inverse Gaussian distribution","volume":"5","author":"TR Tsai","year":"2022","journal-title":"Stats."},{"key":"pone.0327743.ref048","doi-asserted-by":"crossref","first-page":"100353","DOI":"10.1016\/j.health.2024.100353","article-title":"A comparative study of machine learning models with LASSO and SHAP feature selection for breast cancer prediction","volume":"6","author":"MdSH Shaon","year":"2024","journal-title":"Healthc Analyt."},{"key":"pone.0327743.ref049","doi-asserted-by":"crossref","first-page":"1084118","DOI":"10.3389\/fnins.2022.1084118","article-title":"Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images","volume":"16","author":"F Gan","year":"2022","journal-title":"Front Neurosci."},{"issue":"1","key":"pone.0327743.ref050","doi-asserted-by":"crossref","first-page":"239","DOI":"10.3390\/s24010239","article-title":"In-depth evaluation of saliency maps for interpreting convolutional neural network decisions in the diagnosis of glaucoma based on fundus imaging","volume":"24","author":"J Sigut","year":"2023","journal-title":"Sensors (Basel)."},{"issue":"1","key":"pone.0327743.ref051","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"J Bergstra","year":"2012","journal-title":"J Mach Learn Res."},{"key":"pone.0327743.ref052","doi-asserted-by":"crossref","unstructured":"Zhu W, Qiu P, Chen X, Li X, Lepore N, Dumitrascu OM, et al. nnMobileNet: rethinking CNN for retinopathy research. Conf Comput Vis Pattern Recognit Workshops. 2024;2024:2285\u201394. https:\/\/doi.org\/10.1109\/CVPRW63382.2024.00234 40356800","DOI":"10.1109\/CVPRW63382.2024.00234"},{"issue":"4","key":"pone.0327743.ref053","doi-asserted-by":"crossref","first-page":"91","DOI":"10.3390\/computation13040091","article-title":"Eye care: predicting eye diseases using deep learning based on retinal images","volume":"13","author":"A Tashkandi","year":"2025","journal-title":"Computation."},{"key":"pone.0327743.ref054","article-title":"Lightweight convolutional neural networks for retinal disease classification","author":"DK Qasim","year":"2025","journal-title":"arXiv preprint"},{"key":"pone.0327743.ref055","doi-asserted-by":"crossref","unstructured":"Ullah N, Bustamam A, Sarwinda D, Josan GA. Glaucoma detection from fundus images using InceptionV3: a comprehensive study across multiple datasets. In: 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). 2024. p. 1101\u20137. https:\/\/doi.org\/10.1109\/isriti64779.2024.10963631","DOI":"10.1109\/ISRITI64779.2024.10963631"},{"issue":"1","key":"pone.0327743.ref056","article-title":"Detection of diabetic retinopathy from retinal images using DenseNet models","volume":"45","author":"R Nandakumar","year":"2023","journal-title":"Comput Syst Sci Eng."},{"key":"pone.0327743.ref057","doi-asserted-by":"crossref","unstructured":"Hossen MS, Reza AA, Mishu MC. An automated model using deep convolutional neural network for retinal image classification to detect diabetic retinopathy. In: Proceedings of the International Conference on Computing Advancements. 2020. p. 1\u20138. https:\/\/doi.org\/10.1145\/3377049.3377067","DOI":"10.1145\/3377049.3377067"},{"issue":"7","key":"pone.0327743.ref058","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0254180","article-title":"Diagnosis of retinal disorders from optical coherence tomography images using CNN","volume":"16","author":"N Rajagopalan","year":"2021","journal-title":"PLoS One."},{"issue":"9","key":"pone.0327743.ref059","article-title":"Convolutional neural network modeling for eye disease recognition","volume":"18","author":"MAA Siddique","year":"2022","journal-title":"Int J Online Biomed Eng."},{"issue":"1","key":"pone.0327743.ref060","article-title":"Early detection of glaucoma: feature visualization with a deep convolutional network","volume":"12","author":"J N K","year":"2024","journal-title":"Comput Methods Biomech Biomed Eng: Imaging Visualiz."},{"issue":"7","key":"pone.0327743.ref061","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.3390\/biomedicines12071394","article-title":"Deep learning evaluation of glaucoma detection using fundus photographs in highly myopic populations","volume":"12","author":"Y-Y Chiang","year":"2024","journal-title":"Biomedicines."},{"key":"pone.0327743.ref062","doi-asserted-by":"crossref","first-page":"107834","DOI":"10.1016\/j.compbiomed.2023.107834","article-title":"Deep learning innovations in diagnosing diabetic retinopathy: the potential of transfer learning and the DiaCNN model","volume":"169","author":"MR Shoaib","year":"2024","journal-title":"Comput Biol Med."},{"issue":"1","key":"pone.0327743.ref063","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1186\/s12911-024-02518-y","article-title":"A hybrid framework for glaucoma detection through federated machine learning and deep learning models","volume":"24","author":"A Aljohani","year":"2024","journal-title":"BMC Med Inform Decis Mak."},{"key":"pone.0327743.ref064","doi-asserted-by":"crossref","first-page":"108160","DOI":"10.1016\/j.cmpb.2024.108160","article-title":"Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading","volume":"249","author":"R Romero-Ora\u00e1","year":"2024","journal-title":"Comput Methods Prog Biomed."},{"issue":"1","key":"pone.0327743.ref065","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s11517-019-02066-y","article-title":"AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images","volume":"58","author":"AM Alqudah","year":"2020","journal-title":"Med Biol Eng Comput."},{"issue":"10","key":"pone.0327743.ref066","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.3390\/math12101445","article-title":"Age-related macular degeneration detection in retinal fundus images by a deep convolutional neural network","volume":"12","author":"A Garc\u00eda-Floriano","year":"2024","journal-title":"Mathematics."},{"key":"pone.0327743.ref067","article-title":"Classification of diabetic retinopathy severity in fundus images with DenseNet121 and ResNet50","author":"J Zhang","year":"2021","journal-title":"arXiv preprint"},{"issue":"20","key":"pone.0327743.ref068","doi-asserted-by":"crossref","first-page":"3236","DOI":"10.3390\/diagnostics13203236","article-title":"HDR-EfficientNet: a classification of hypertensive and diabetic retinopathy using optimize EfficientNet architecture","volume":"13","author":"Q Abbas","year":"2023","journal-title":"Diagnostics (Basel)."},{"key":"pone.0327743.ref069","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1038\/s41746-019-0172-3","article-title":"Deep learning algorithm predicts diabetic retinopathy progression in individual patients","volume":"2","author":"F Arcadu","year":"2019","journal-title":"NPJ Digit Med."},{"issue":"22","key":"pone.0327743.ref070","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"DSW Ting","year":"2017","journal-title":"JAMA."},{"issue":"2","key":"pone.0327743.ref071","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"DSW Ting","year":"2019","journal-title":"Br J Ophthalmol."}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0327743","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T17:33:13Z","timestamp":1754069593000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0327743"}},"subtitle":[],"editor":[{"given":"Ogugua","family":"Ndubuisi Okonkwo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"references-count":71,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8,1]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0327743","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,1]]}}}