{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T16:47:04Z","timestamp":1781023624853,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T00:00:00Z","timestamp":1745193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Secretar\u00eda de Ciencia, Humanidades, Tecnolog\u00eda e Innovaci\u00f3n de M\u00e9xico (SECIHTI)","award":["3097-7185"],"award-info":[{"award-number":["3097-7185"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Early detection of diabetic retinopathy is critical for preserving vision in diabetic patients. The classification of lesions in Retinal fundus images, particularly macular edema, is an essential diagnostic tool, yet it presents a significant learning curve for both novice and experienced ophthalmologists. To address this challenge, a novel Convolutional Deep Belief Network (CDBN) is proposed to classify image patches into three distinct categories: two types of macular edema\u2014microhemorrhages and hard exudates\u2014and a healthy category. The method leverages high-level feature extraction to mitigate issues arising from the high similarity of low-level features in noisy images. Additionally, a Real-Coded Genetic Algorithm optimizes the parameters of Gabor filters and the network, ensuring optimal feature extraction and classification performance. Experimental results demonstrate that the proposed CDBN outperforms comparative models, achieving an F1 score of 0.9258. These results indicate that the architecture effectively overcomes the challenges of lesion classification in retinal images, offering a robust tool for clinical application and paving the way for advanced clinical decision support systems in diabetic retinopathy management.<\/jats:p>","DOI":"10.3390\/jimaging11040123","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T04:57:11Z","timestamp":1745211431000},"page":"123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evolutionary-Driven Convolutional Deep Belief Network for the Classification of Macular Edema in Retinal Fundus Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6280-4751","authenticated-orcid":false,"given":"Rafael A.","family":"Garc\u00eda-Ram\u00edrez","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n en Matem\u00e1ticas (CIMAT), Guanajuato 36023, Guanajuato, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5197-2059","authenticated-orcid":false,"given":"Ivan","family":"Cruz-Aceves","sequence":"additional","affiliation":[{"name":"SECIHTI-Centro de Investigaci\u00f3n en Matem\u00e1ticas, Guanajuato 36023, Guanajuato, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3744-9827","authenticated-orcid":false,"given":"Arturo","family":"Hern\u00e1ndez-Aguirre","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Matem\u00e1ticas (CIMAT), Guanajuato 36023, Guanajuato, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1810-9587","authenticated-orcid":false,"given":"Gloria P.","family":"Trujillo-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Unidad Medica de Alta Especialidad, Hospital de Especialidades No. 1 IMSS, Le\u00f3n 37320, Guanajuato, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6903-2233","authenticated-orcid":false,"given":"Martha A.","family":"Hernandez-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Divisi\u00f3n de Ciencias de la Salud, Universidad de Guanajuato, Campus Le\u00f3n, Le\u00f3n 37544, Guanajuato, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/5584_2020_535","article-title":"Diabetic Macular Edema: State of Art and Intraocular Pharmacological Approaches","volume":"1307","author":"Gurreri","year":"2021","journal-title":"Adv. Exp. Med. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1038\/nrendo.2017.151","article-title":"Global aetiology and epidemiology of type 2 diabetes mellitus and its complications","volume":"14","author":"Zheng","year":"2018","journal-title":"Nat. Rev. Endocrinol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1038\/eye.2017.64","article-title":"Oxidative stress and diabetic retinopathy: Development and treatment","volume":"31","author":"Calderon","year":"2017","journal-title":"Eye"},{"key":"ref_4","first-page":"159","article-title":"Diabetic Retinopathy: Vascular Pathophysiology and Emerging Therapies","volume":"3","author":"Ciulla","year":"2015","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_5","first-page":"100823","article-title":"Diabetic Macular Edema: Advances in Pathophysiology and Treatment Strategies","volume":"75","year":"2021","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.media.2011.07.004","article-title":"Exudate-based diabetic macular edema detection in fundus images using publicly available datasets","volume":"16","author":"Giancardo","year":"2012","journal-title":"Med. Image Anal."},{"key":"ref_8","first-page":"482","article-title":"Automated Retinal Lesion Detection Using Deep Learning","volume":"37","author":"Quellec","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sundaram, S., Selvamani, M., Raju, S.K., Ramaswamy, S., Islam, S., Cha, J.H., Almujally, N.A., and Elaraby, A. (2023). Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks. Diagnostics, 13.","DOI":"10.3390\/diagnostics13051001"},{"key":"ref_10","unstructured":"Nguyen, D.M.H., Alam, H.M.T., Nguyen, T., Srivastav, D., Profitlich, H.J., Le, N., and Sonntag, D. (2025). Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Parmar, U.P.S., Surico, P.L., Singh, R.B., Romano, F., Salati, C., Spadea, L., Musa, M., Gagliano, C., Mori, T., and Zeppieri, M. (2024). Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases. Medicina, 60.","DOI":"10.3390\/medicina60040527"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mutawa, A.M., Alnajdi, S., and Sruthi, S. (2023). Transfer Learning for Diabetic Retinopathy Detection: A Study of Dataset Combination and Model Performance. Appl. Sci., 13.","DOI":"10.3390\/app13095685"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s44147-023-00335-0","article-title":"Automatic multi-disease classification on retinal images using multilevel glowworm swarm convolutional neural network","volume":"71","author":"Chavan","year":"2024","journal-title":"J. Eng. Appl. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"\u00dcnver, H.M., K\u00f6kver, Y., Duman, E., and Erdem, O.A. (2019). Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization. Appl. Sci., 9.","DOI":"10.3390\/app9020350"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Almotiri, J., Elleithy, K., and Elleithy, A. (2018). Retinal Vessels Segmentation Techniques and Algorithms: A Survey. Appl. Sci., 8.","DOI":"10.3390\/app8020155"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Alvarez, L., Mejail, M., Gomez, L., and Jacobo, J. (2012). An Introduction to Restricted Boltzmann Machines. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Springer. CIARP 2012, Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-642-33275-3"},{"key":"ref_17","first-page":"1641","article-title":"Energy-Based Models in Deep Learning: A Survey","volume":"41","author":"LeCun","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","first-page":"1569","article-title":"Deep Belief Networks: Advances, Challenges, and Applications","volume":"28","author":"Hinton","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lee, H., Grosse, R., Ranganath, R., and Ng, A.Y. (2009, January 14\u201318). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th Annual International Conference on Machine Learning, ICML\u201909, New York, NY, USA.","DOI":"10.1145\/1553374.1553453"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD\u201916, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.ins.2013.02.040","article-title":"A Boltzmann based estimation of distribution algorithm","volume":"236","author":"Valdez","year":"2013","journal-title":"Inf. Sci."},{"key":"ref_22","unstructured":"Lu, B.L., Zhang, L., and Kwok, J. (2011, January 13\u201317). Univariate Marginal Distribution Algorithm in Combination with Extremal Optimization (EO, GEO). Proceedings of the Neural Information Processing, Shanghai, China."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dong, K., Zhou, C., Ruan, Y., and Li, Y. (2020, January 18\u201320). MobileNetV2 Model for Image Classification. Proceedings of the 2020 2nd International Conference on Information Technology and Computer Application (ITCA), Guangzhou, China.","DOI":"10.1109\/ITCA52113.2020.00106"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 22\u201325). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018). Learning Transferable Architectures for Scalable Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. arXiv.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2016). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. arXiv.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2015). Rethinking the Inception Architecture for Computer Vision. arXiv.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019). Searching for MobileNetV3. arXiv.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_30","unstructured":"Tan, M., and Le, Q.V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv."},{"key":"ref_31","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_32","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022). A ConvNet for the 2020s. arXiv.","DOI":"10.1109\/CVPR52688.2022.01167"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/4\/123\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:18:37Z","timestamp":1760030317000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/4\/123"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,21]]},"references-count":33,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["jimaging11040123"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11040123","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,21]]}}}