{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T16:31:28Z","timestamp":1789576288882,"version":"build-2803163510"},"reference-count":53,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"KAU Endowment (WAQF) at King Abdulaziz University","award":["269-612-2024"],"award-info":[{"award-number":["269-612-2024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Glaucoma is an eye disease caused by increased intraocular pressure (IOP) that affects the optic nerve head (ONH), leading to vision problems and irreversible blindness. Background\/Objectives: Glaucoma is the second leading cause of blindness worldwide, and the number of people affected is increasing each year, with the number expected to reach 111.8 million by 2040. This escalating trend is alarming due to the lack of ophthalmology specialists relative to the population. This study proposes an explainable end-to-end pipeline for automated glaucoma diagnosis from fundus images. It also evaluates the performance of Vision Transformers (ViTs) relative to traditional CNN-based models. Methods: The proposed system uses three datasets: REFUGE, ORIGA, and G1020. It begins with YOLOv11 for object detection of the optic disc. Then, the optic disc (OD) and optic cup (OC) are segmented using U-Net with ResNet50, VGG16, and MobileNetV2 backbones, as well as MaskFormer with a Swin-Base backbone. Glaucoma is classified based on the vertical cup-to-disc ratio (vCDR). Results: MaskFormer outperforms all models in segmentation in all aspects, including IoU OD, IoU OC, DSC OD, and DSC OC, with scores of 88.29%, 91.09%, 93.83%, and 93.71%. For classification, it achieved accuracy and F1-scores of 84.03% and 84.56%. Conclusions: By relying on the interpretable features of the vCDR, the proposed framework enhances transparency and aligns well with the principles of explainable AI, thus offering a trustworthy solution for glaucoma screening. Our findings show that Vision Transformers offer a promising approach for achieving high segmentation performance with explainable, biomarker-driven diagnosis.<\/jats:p>","DOI":"10.3390\/diagnostics15182301","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T15:39:23Z","timestamp":1757518763000},"page":"2301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Explainable Transformer-Based Framework for Glaucoma Detection from Fundus Images Using Multi-Backbone Segmentation and vCDR-Based Classification"],"prefix":"10.3390","volume":"15","author":[{"given":"Hind","family":"Alasmari","sequence":"first","affiliation":[{"name":"Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5640-7166","authenticated-orcid":false,"given":"Ghada","family":"Amoudi","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2059-7687","authenticated-orcid":false,"given":"Hanan","family":"Alghamdi","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47411","DOI":"10.1109\/ACCESS.2021.3068204","article-title":"The Use of U-Net Lite and Extreme Gradient Boost (XGB) for Glaucoma Detection","volume":"9","author":"Afolabi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"140031","DOI":"10.1109\/ACCESS.2021.3118102","article-title":"Real-Time Glaucoma Detection From Digital Fundus Images Using Self-ONNs","volume":"9","author":"Devecioglu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kashyap, R., Nair, R., Gangadharan, S.M.P., Botto-Tobar, M., Farooq, S., and Rizwan, A. (2022). Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model. Healthcare, 10.","DOI":"10.3390\/healthcare10122497"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nawaz, M., Nazir, T., Javed, A., Tariq, U., Yong, H.-S., Khan, M.A., and Cha, J. (2022). An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization. Sensors, 22.","DOI":"10.3390\/s22020434"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chayan, T.I., Islam, A., Rahman, E., Reza, M.T., Apon, T.S., and Alam, M.G.R. (2022, January 18). Explainable AI Based Glaucoma Detection Using Transfer Learning and LIME. Proceedings of the 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia.","DOI":"10.1109\/CSDE56538.2022.10089310"},{"key":"ref_6","first-page":"100038","article-title":"Glaucoma Detection in Retinal Fundus Images Using U-Net and Supervised Machine Learning Algorithms","volume":"5","author":"Shinde","year":"2021","journal-title":"Intell.-Based Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhu, J., Zhang, E., and Del Rio-Tsonis, K. (2012). Eye Anatomy. Encyclopedia of Life Sciences, Wiley.","DOI":"10.1002\/9780470015902.a0000108.pub2"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"92","DOI":"10.17925\/EOR.2012.06.02.92","article-title":"Clinical Optic Disc Evaluation in Glaucoma","volume":"6","author":"Reis","year":"2012","journal-title":"Eur. Ophthalmic Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1007\/s42452-020-03221-z","article-title":"A Review on the Optic Disc and Optic Cup Segmentation and Classification Approaches over Retinal Fundus Images for Detection of Glaucoma","volume":"2","author":"Veena","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, M., Liu, H., Mercieca, K., Prinz, J., Feng, Y., and Prokosch, V. (2023). The Association between Vascular Abnormalities and Glaucoma\u2014What Comes First?. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms241713211"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ajo.2018.11.012","article-title":"Macula Vessel Density and Thickness in Early Primary Open-Angle Glaucoma","volume":"199","author":"Hou","year":"2019","journal-title":"Am. J. Ophthalmol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Barros, D.M.S., Moura, J.C.C., Freire, C.R., Taleb, A.C., Valentim, R.A.M., and Morais, P.S.G. (2020). Machine Learning Applied to Retinal Image Processing for Glaucoma Detection: Review and Perspective. Biomed. Eng. OnLine, 19.","DOI":"10.1186\/s12938-020-00767-2"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tadisetty, S., Chodavarapu, R., Jin, R., Clements, R.J., and Yu, M. (2023). Identifying the Edges of the Optic Cup and the Optic Disc in Glaucoma Patients by Segmentation. Sensors, 23.","DOI":"10.3390\/s23104668"},{"key":"ref_14","first-page":"235","article-title":"Optic Disc Evaluation in Glaucoma","volume":"44","year":"1996","journal-title":"Indian J. Ophthalmol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ahn, J.M., Kim, S., Ahn, K.-S., Cho, S.-H., Lee, K.B., and Kim, U.S. (2018). A Deep Learning Model for the Detection of Both Advanced and Early Glaucoma Using Fundus Photography. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0207982"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1167\/tvst.9.2.42","article-title":"A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression","volume":"9","author":"Thompson","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"26731","DOI":"10.1007\/s11042-022-14305-w","article-title":"Machine Learning and Deep Learning Approach for Medical Image Analysis: Diagnosis to Detection","volume":"82","author":"Rana","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s40537-022-00652-w","article-title":"Transfer Learning: A Friendly Introduction","volume":"9","author":"Hosna","year":"2022","journal-title":"J. Big Data"},{"key":"ref_19","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale 2020. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows 2021. arXiv.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_21","unstructured":"Cheng, B., Schwing, A.G., and Kirillov, A. (2021). Per-Pixel Classification Is Not All You Need for Semantic Segmentation 2021. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object Detection in 20 Years: A Survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/TMI.2013.2247770","article-title":"Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening","volume":"32","author":"Cheng","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"15531","DOI":"10.1007\/s11042-019-7460-4","article-title":"Automated Detection of Glaucoma Using Deep Learning Convolution Network (G-Net)","volume":"79","author":"Juneja","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","article-title":"Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.procs.2021.12.040","article-title":"Optic Disc and Cup Segmentations for Glaucoma Assessment Using Cup-to-Disc Ratio","volume":"196","author":"Neto","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"127519","DOI":"10.1109\/ACCESS.2020.3008539","article-title":"Dual Machine-Learning System to Aid Glaucoma Diagnosis Using Disc and Cup Feature Extraction","volume":"8","author":"Civit","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","unstructured":"Agrawal, V., Kori, A., Alex, V., and Krishnamurthi, G. (2018). Enhanced Optic Disk and Cup Segmentation with Glaucoma Screening from Fundus Images Using Position Encoded CNNs 2018. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sreng, S., Maneerat, N., Hamamoto, K., and Win, K.Y. (2020). Deep Learning for Optic Disc Segmentation and Glaucoma Diagnosis on Retinal Images. Appl. Sci., 10.","DOI":"10.3390\/app10144916"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Velpula, V.K., and Sharma, L.D. (2023). Multi-Stage Glaucoma Classification Using Pre-Trained Convolutional Neural Networks and Voting-Based Classifier Fusion. Front. Physiol., 14.","DOI":"10.3389\/fphys.2023.1175881"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chincholi, F., and Koestler, H. (2024). Transforming Glaucoma Diagnosis: Transformers at the Forefront. Front. Artif. Intell., 7.","DOI":"10.3389\/frai.2024.1324109"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"101570","DOI":"10.1016\/j.media.2019.101570","article-title":"REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs","volume":"59","author":"Orlando","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_33","unstructured":"Zhang, Z., Yin, F.S., Liu, J., Wong, W.K., Tan, N.M., Lee, B.H., Cheng, J., and Wong, T.Y. (September, January 31). ORIGA-light: An Online Retinal Fundus Image Database for Glaucoma Analysis and Research. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bajwa, M.N., Singh, G.A.P., Neumeier, W., Malik, M.I., Dengel, A., and Ahmed, S. (2020). G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection 2020. arXiv.","DOI":"10.1109\/IJCNN48605.2020.9207664"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"12561","DOI":"10.1007\/s10462-023-10453-z","article-title":"Medical Image Data Augmentation: Techniques, Comparisons and Interpretations","volume":"56","author":"Goceri","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"187536","DOI":"10.1109\/ACCESS.2024.3470122","article-title":"Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions","volume":"12","author":"Kumar","year":"2024","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Alomar, K., Aysel, H.I., and Cai, X. (2023). Data Augmentation in Classification and Segmentation: A Survey and New Strategies. J. Imaging, 9.","DOI":"10.3390\/jimaging9020046"},{"key":"ref_38","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_39","unstructured":"Jocher, G., and Qiu, J. (2024, November 20). Ultralytics YOLO11. Available online: https:\/\/docs.ultralytics.com\/models\/yolo11\/#citations-and-acknowledgements."},{"key":"ref_40","unstructured":"(2024, November 10). Leading Data Annotation Platform. Available online: https:\/\/www.cvat.ai\/."},{"key":"ref_41","unstructured":"Solawetz, J. (2025, March 22). What Is CVAT (Computer Vision Annotation Tool)?. Available online: https:\/\/blog.roboflow.com\/cvat\/."},{"key":"ref_42","unstructured":"(2025, June 16). COCO JSON. Available online: https:\/\/roboflow.com\/formats\/coco-json?utm_source=chatgpt.com."},{"key":"ref_43","unstructured":"(2025, June 16). YOLO Darknet TXT. Available online: https:\/\/roboflow.com\/formats\/yolo-darknet-txt?utm_source=chatgpt.com."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Padilla, R., Netto, S.L., and Da Silva, E.A.B. (2020, January 1\u20133). A Survey on Performance Metrics for Object-Detection Algorithms. Proceedings of the 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Niter\u00f3i, Brazil.","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hosang, J., Benenson, R., and Schiele, B. (2017). Learning Non-Maximum Suppression 2017. arXiv.","DOI":"10.1109\/CVPR.2017.685"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"M\u00fcller, D., Soto-Rey, I., and Kramer, F. (2022). Towards a Guideline for Evaluation Metrics in Medical Image Segmentation. BMC Res. Notes, 15.","DOI":"10.1186\/s13104-022-06096-y"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"295","DOI":"10.70594\/brain\/15.4\/20","article-title":"Selecting the Right Metric: A Detailed Study on Image Segmentation Evaluation","volume":"15","author":"Boiangiu","year":"2024","journal-title":"BRAIN Broad Res. Artif. Intell. Neurosci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Khouy, M., Jabrane, Y., Ameur, M., and Hajjam El Hassani, A. (2023). Medical Image Segmentation Using Automatic Optimized U-Net Architecture Based on Genetic Algorithm. J. Pers. Med., 13.","DOI":"10.3390\/jpm13091298"},{"key":"ref_49","unstructured":"(2024, March 12). Vuppala Adithya Sairam OC\/OD Segmentation Using Keras. Available online: https:\/\/www.kaggle.com\/code\/vuppalaadithyasairam\/oc-od-segmentation-using-keras."},{"key":"ref_50","unstructured":"(2025, April 20). MaskFormer. Available online: https:\/\/huggingface.co\/facebook\/maskformer-swin-base-ade."},{"key":"ref_51","unstructured":"Iakubovskii, P., and Montalvo, P. (2025, April 14). Semantic Segmentation Examples. Available online: https:\/\/github.com\/huggingface\/transformers\/blob\/main\/examples\/pytorch\/semantic-segmentation\/README.md."},{"key":"ref_52","unstructured":"Rogge, N. (2025, April 12). Fine-tuning MaskFormer for Instance Segmentation on Semantic Sidewalk Dataset. Available online: https:\/\/github.com\/NielsRogge\/Transformers-Tutorials\/blob\/master\/MaskFormer\/Fine-tuning\/Fine_tuning_MaskFormerForInstanceSegmentation_on_semantic_sidewalk.ipynb."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1109\/TMI.2018.2837012","article-title":"Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/18\/2301\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:43:29Z","timestamp":1760035409000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/18\/2301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,10]]},"references-count":53,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["diagnostics15182301"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15182301","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,10]]}}}