Diabetic Retinopathy Classification Using Deep Learning
Keywords:Diabetic retinopathy, DR, deep learning, automated system, fundus images
One of the main causes of adult blindness and a frequent consequence of diabetes is diabetic retinopathy (DR). To avoid visual loss, DR must be promptly identified and classified. In this article, we suggest an automated DR detection and classification method based on deep learning applied to fundus pictures. The suggested technique uses transfer learning for classification. On a dataset of 3,662 fundus images with real-world DR severity labels, we trained and validated our model. According to our findings, the suggested technique successfully detected and classified DR with an overall accuracy of 78.14%. Our model fared better than other recent cutting-edge techniques, illuminating the promise of deep learning-based strategies for DR detection and management. Our research indicates that the suggested technique may be employed as a screening tool for DR in a clinical environment, enabling early illness diagnosis and prompt treatment.
Shulha, M., Gordienko, Y., Stirenko, S. (2023). Impact of Multimodal Model Complexity on Classification of Diabetic Retinopathy Level. In: García Márquez, F.P., Jamil, A., Eken, S., Hameed, A.A. (eds) Computational Intelligence, Data Analytics and Applications. ICCIDA 2022. Lecture Notes in Networks and Systems, vol 643. Springer, Cham.
Mukherjee, N., Sengupta, S. (2022). Comparing Different Preprocessing Techniques for the Classification Tasks in Diabetic Retinopathy from Fundus Images. In: Mandal, J.K., Buyya, R., De, D. (eds) Proceedings of International Conference on Advanced Computing Applications. Advances in Intelligent Systems and Computing, vol 1406. Springer, Singapore.
Firke, S.N. and Jain, R.B., 2021, March. Convolutional Neural Network for Diabetic Retinopathy Detection. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) (pp. 549-553). IEEE.
Sambyal, N., Saini, P., Syal, R. (2022). A Discriminative Learning-Based Deep Learning Approach for Diabetic Retinopathy Classification. In: Sanyal, G., Travieso-González, C.M., Awasthi, S., Pinto, C.M., Purushothama, B.R. (eds) International Conference on Artificial Intelligence and Sustainable Engineering. Lecture Notes in Electrical Engineering, vol 837. Springer, Singapore.
Thomas, N.M., Albert Jerome, S. (2022). Grading and Classification of Retinal Images for Detecting Diabetic Retinopathy Using Convolutional Neural Network. In: Sengodan, T., Murugappan, M., Misra, S. (eds) Advances in Electrical and Computer Technologies. ICAECT 2021. Lecture Notes in Electrical Engineering, vol 881. Springer, Singapore.
Shulha, M., Gordienko, Y., Stirenko, S. (2023). Deep Learning with Metadata Augmentation for Classification of Diabetic Retinopathy Level. In: Shakya, S., Balas, V.E., Haoxiang, W. (eds) Proceedings of Third International Conference on Sustainable Expert Systems . Lecture Notes in Networks and Systems, vol 587. Springer, Singapore.
Naveenkumar, M., Srithar, S., Maheswaran, T., Sivapriya, K., Brinda, B.M. (2022). Diabetic Retinopathy Disease Classification Using EfficientNet-B3. In: Raj, J.S., Kamel, K., Lafata, P. (eds) Innovative Data Communication Technologies and Application. Lecture Notes on Data Engineering and Communications Technologies, vol 96. Springer, Singapore.
Kapoor, P., Arora, S. (2022). Applications of Deep Learning in Diabetic Retinopathy Detection and Classification: A Critical Review. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 91. Springer, Singapore.
S. Tiwari, A. Shukla, A. Jain and A. Alferaidi, "Broad Analysis of Deep Learning Techniques for Diabetic Retinopathy Screening," 2023 International Conference on Smart Computing and Application (ICSCA), Hail, Saudi Arabia, 2023, pp. 1-5, doi: 10.1109/ICSCA57840.2023.10087482.
P. Hatode, M. M. Edinburgh and M. Jha, "Evolution and Testimony of Deep Learning Algorithm for Diabetic Retinopathy Detection," 2022 5th International Conference on Advances in Science and Technology (ICAST), Mumbai, India, 2022, pp. 122-126, doi: 10.1109/ICAST55766.2022.10039538.
P. Shetgaonkar, S. Aswale, S. Naik, A. Gaonkar, S. Gawade and P. Mhalsekar, "Diabetic Retinopathy Detection and Classification from Fundus Images Using Deep Learning," 2021 International Conference on Technological Advancements and Innovations (ICTAI), Tashkent, Uzbekistan, 2021, pp. 133-139, doi: 10.1109/ICTAI53825.2021.9673345.
S. Das, D. Das, S. K. Biswas and B. Purkayastha, "Deep Diabetic Retinopathy Detection System (DDRDS) using Convolutional Neural Network: A Comparative Study," 2021 International Conference on Intelligent Technologies (CONIT), Hubli, India, 2021, pp. 1-5, doi: 10.1109/CONIT51480.2021.9498420.
L. R and A. Padyana, "Detection of Diabetic Retinopathy in Retinal Fundus Image Using YOLO-RF Model," 2021 Sixth International Conference on Image Information Processing (ICIIP), Shimla, India, 2021, pp. 105-109, doi: 10.1109/ICIIP53038.2021.9702677.
C. Harshitha, A. Asha, J. L. S. Pushkala, R. N. S. Anogini and K. C, "Predicting the Stages of Diabetic Retinopathy using Deep Learning," 2021 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2021, pp. 1-6, doi: 10.1109/ICICT50816.2021.9358801.
V. S and V. R, "A Survey on Diabetic Retinopathy Disease Detection and Classification using Deep Learning Techniques," 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India, 2021, pp. 1-4, doi: 10.1109/ICBSII51839.2021.9445163.
P. Furtado, "Multi-class segmentation of Diabetic Retinopathy lesions: effects of metrics, improvements and loss," 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 2020, pp. 1410-1417, doi: 10.1109/ICMLA51294.2020.00219.
S. Mishra, S. Hanchate and Z. Saquib, "Diabetic Retinopathy Detection using Deep Learning," 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), Bengaluru, India, 2020, pp. 515-520, doi: 10.1109/ICSTCEE49637.2020.9277506.
S. Gupta, A. Panwar, S. Goel, A. Mittal, R. Nijhawan and A. K. Singh, "Classification of Lesions in Retinal Fundus Images for Diabetic Retinopathy Using Transfer Learning," 2019 International Conference on Information Technology (ICIT), Bhubaneswar, India, 2019, pp. 342-347, doi: 10.1109/ICIT48102.2019.00067.
Jena, P.K.; Khuntia, B.; Palai, C.; Nayak, M.; Mishra, T.K.; Mohanty, S.N. A Novel Approach for Diabetic Retinopathy Screening Using Asymmetric Deep Learning Features. Big Data Cogn. Comput. 2023, 7, 25. https://doi.org/10.3390/bdcc7010025.
Lokesh,K.,Challa,N.P.,Satwik,A.S.,Kiran,J.C.,Kumar Rao, N., & Naseeba, B. (2023). Early Alzheimer’s Disease Detection Using Deep Learning . EAI Endorsed Transactions on Pervasive Health and Technology, 9. https://doi.org/10.4108/eetpht.9.3966
A. S. Sathwik, B. Naseeba and N. P. Challa, "Cardiovascular Disease Prediction Using Hybrid-Random- Forest- Linear- Model (HRFLM)," 2023 IEEE World Conference on Applied Intelligence and Computing (AIC), Sonbhadra, India, 2023, pp. 192-197, doi: 10.1109/AIC57670.2023.10263865.
Agarwal, R., Suthar, J., Panda, S. K., & Mohanty, S. N. (2023). Fuzzy and Machine Learning based Multi-Criteria Decision Making for Selecting Electronics Product. EAI Endorsed Transactions on Scalable Information Systems, 10(5). https://doi.org/10.4108/eetsis.3353
Agarwal, R., & Godavarthi, D. (2023). Skin Disease Classification Using CNN Algorithms. EAI Endorsed Transactions on Pervasive Health and Technology, 9. https://doi.org/10.4108/eetpht.9.4039
Chandrahaas, B. V., Mohanty, S. N., Panda, S. K., & Michael, G. (2023). An Empirical Study on Classification of Monkeypox Skin Lesion Detection. EAI Endorsed Transactions on Pervasive Health and Technology, 9(1).
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