{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:46:59Z","timestamp":1784738819572,"version":"3.55.0"},"reference-count":27,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T00:00:00Z","timestamp":1663286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Vellore Institute of Technology (VIT)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Diabetic Retinopathy (DR) is an eye condition that mainly affects individuals who have diabetes and is one of the important causes of blindness in adults. As the infection progresses, it may lead to permanent loss of vision. Diagnosing diabetic retinopathy manually with the help of an ophthalmologist has been a tedious and a very laborious procedure. This paper not only focuses on diabetic retinopathy detection but also on the analysis of different DR stages, which is performed with the help of Deep Learning (DL) and transfer learning algorithms. CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet are used on a huge dataset with around 3662 train images to automatically detect which stage DR has progressed. Five DR stages, which are 0 (No DR), 1 (Mild DR), 2 (Moderate), 3 (Severe) and 4 (Proliferative DR) are processed in the proposed work. The patient\u2019s eye images are fed as input to the model. The proposed deep learning architectures like CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet 2.1 are used to extract the features of the eye for effective classification. The models achieved an accuracy of 96.22%, 93.18% and 75.61% respectively. The paper concludes with a comparative study of the CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet architectures that highlights hybrid CNN with DenseNet as the perfect deep learning classification model for automated DR detection.<\/jats:p>","DOI":"10.3390\/sym14091932","type":"journal-article","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T03:48:04Z","timestamp":1663300084000},"page":"1932","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":97,"title":["Diabetic Retinopathy Classification Using CNN and Hybrid Deep Convolutional Neural Networks"],"prefix":"10.3390","volume":"14","author":[{"given":"Yasashvini","family":"R.","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vergin","family":"Raja Sarobin M.","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rukmani","family":"Panjanathan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Graceline Jasmine","family":"S.","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8904-2236","authenticated-orcid":false,"given":"Jani Anbarasi","family":"L.","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"29383","DOI":"10.1007\/s11042-022-12916-x","article-title":"Intelligent automation of invoice parsing using computer vision techniques","volume":"81","author":"Chazhoor","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22263","DOI":"10.1007\/s11042-021-11257-5","article-title":"Narayanan, S. Detection of novel coronavirus from chest X-rays using deep convolutional neural networks","volume":"81","author":"Sanket","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15583","DOI":"10.48048\/wjst.2021.15583","article-title":"Predictive Analytics of COVID-19 Pandemic: Statistical Modelling Perspective","volume":"18","author":"Kumar","year":"2021","journal-title":"Walailak J. Sci. Technol. (WJST)"},{"key":"ref_4","unstructured":"(2022, June 13). Available online: https:\/\/www.kaggle.com\/competitions\/aptos2019-blindness-detection\/data?select=test_images."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jiang, H., Yang, K., Gao, M., Zhang, D., Ma, H., and Qian, W. (2019, January 23\u201327). An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification. Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857160"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Roy, A., Dutta, D., Bhattacharya, P., and Choudhury, S. (2017, January 6\u20138). Filter and fuzzy c means based feature extraction and classification of diabetic retinopathy using support vector machines. Proceedings of the International Conference on Communication and Signal Processing (ICCSP), Tamil Nadu, India.","DOI":"10.1109\/ICCSP.2017.8286715"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Qian, Z., Wu, C., Chen, H., and Chen, M. (2021, January 12\u201314). Diabetic Retinopathy Grading Using Attention based Convolution Neural Network. Proceedings of the IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC50856.2021.9390963"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"AbdelMaksoud, E., Barakat, S., and Elmogy, M. (2020, January 26\u201327). Diabetic Retinopathy Grading Based on a Hybrid Deep Learning Model. Proceedings of the International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), Sakheer, Bahrain.","DOI":"10.1109\/ICDABI51230.2020.9325672"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3391","DOI":"10.1109\/TBME.2013.2278845","article-title":"Assessing the Need for Referral in Automatic Diabetic Retinopathy Detection","volume":"60","author":"Pires","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dua, S., Kandiraju, N., and Thompson, H. (2005, January 4\u20136). Design and implementation of a unique blood-vessel detection algorithm towards early diagnosis of diabetic retinopathy. Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC\u201905)-Volume II, Las Vegas, NV, USA.","DOI":"10.1109\/ITCC.2005.120"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jelinek, H.F., Pires, R., Padilha, R., Goldenstein, S., Wainer, J., Bossomaier, T., and Rocha, A. (2012, January 20\u201322). Data fusion for multi-lesion Diabetic Retinopathy detection. Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS), Rome, Italy.","DOI":"10.1109\/CBMS.2012.6266342"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bourouis, S., Zaguia, A., and Bouguila, N. (2018, January 27\u201329). Hybrid Statistical Framework for Diabetic Retinopathy Detection. Proceedings of the International Conference Image Analysis and Recognition, Varzim, Portugal.","DOI":"10.1007\/978-3-319-93000-8_78"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lachure, J., Deorankar, A., Lachure, S., Gupta, S., and Jadhav, R. (2015, January 12\u201313). Diabetic Retinopathy using morphological operations and machine learning. Proceedings of the IEEE International Advance Computing Conference (IACC), Bangalore, India.","DOI":"10.1109\/IADCC.2015.7154781"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bhatkar, A.P., and Kharat, G. (2015, January 21\u201323). Detection of Diabetic Retinopathy in Retinal Images Using MLP Classifier. Proceedings of the IEEE International Symposium on Nanoelectronic and Information Systems, Indore, India.","DOI":"10.1109\/iNIS.2015.30"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mishra, S., Hanchate, S., and Saquib, Z. (2020, January 9\u201310). Diabetic Retinopathy Detection using Deep Learning. Proceedings of the International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), Bangalore, India.","DOI":"10.1109\/ICSTCEE49637.2020.9277506"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Palavalasa, K.K., and Sambaturu, B. (2018, January 3\u20135). Automatic Diabetic Retinopathy Detection Using Digital Image Processing. Proceedings of the International Conference on Communication and Signal Processing (ICCSP), Chennai, India.","DOI":"10.1109\/ICCSP.2018.8524234"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Prasad, D.K., Vibha, L., and Venugopal, K. (2015, January 10\u201312). Early detection of diabetic retinopathy from digital retinal fundus images. Proceedings of the IEEE Recent Advances in Intelligent Computational Systems (RAICS), Kerala, India.","DOI":"10.1109\/RAICS.2015.7488421"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12010","DOI":"10.1088\/1742-6596\/1722\/1\/012010","article-title":"Detection and description generation of diabetic retinopathy using convolutional neural network and long short-term memory","volume":"1722","author":"Amalia","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nagi, A.T., Awan, M.J., Javed, R., and Ayesha, N. (2021, January 6\u20137). A Comparison of Two-Stage Classifier Algorithm with Ensemble Techniques On Detection of Diabetic Retinopathy In Proceedings of the 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA). Riyadh, Saudi Arabia.","DOI":"10.1109\/CAIDA51941.2021.9425129"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yadav, J., Sharma, M., and Saxena, V. (2017, January 10\u201312). Diabetic retinopathy detection using feedforward neural network. Proceedings of the Tenth International Conference on Contemporary Computing (IC3), Noida, India.","DOI":"10.1109\/IC3.2017.8284350"},{"key":"ref_21","first-page":"1","article-title":"Deep neural networks to predict diabetic retinopathy","volume":"1","author":"Gadekallu","year":"2020","journal-title":"J. Ambient. Intell. Humaniz.Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-018-1111-6","article-title":"Diabetic retinopathy diagnosis from retinal images using modified hopfield neural network","volume":"42","author":"Hemanth","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s13246-021-01012-3","article-title":"Diabetic retinopathy classification based on multipath CNN and machine learning classifiers","volume":"44","author":"Gayathri","year":"2021","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Varghese, N.R., and Gopan, N.R. (2019, January 17\u201318). Performance analysis of automated detection of diabetic retinopathy using machine learning and deep learning techniques. Proceedings of the International Conference on Innovative Data Communication Technologies and Application, Coimbatore, India.","DOI":"10.1007\/978-3-030-38040-3_18"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21\u201323). Understanding of a convolutional neural network. Proceedings of the International Conference on Engineering and Technology (ICET), Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). 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_27","doi-asserted-by":"crossref","first-page":"4203","DOI":"10.1007\/s11042-020-09727-3","article-title":"Vision based inspection system for leather surface defect detection using fast convergence particle swarm optimization ensemble classifier approach","volume":"80","author":"Jawahar","year":"2021","journal-title":"Multimed. Tools Appl."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/9\/1932\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:32:35Z","timestamp":1760142755000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/9\/1932"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,16]]},"references-count":27,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["sym14091932"],"URL":"https:\/\/doi.org\/10.3390\/sym14091932","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,16]]}}}