Advanced Diabetic Retinopathy Detection and Classification Using Lightweight Deep Learning Techniques
Keywords:
Classification, Convolutional Neural Network, Detection, Diabetic Retinopathy, EfficientNetAbstract
Diabetic Retinopathy (DR) is an ocular disorder that has the potential to result in visual impairment and complete loss of vision in those diagnosed with diabetes. This illness affects the retinal blood vessels inside the light-sensitive tissue layer at the posterior of the eye, known as the retina. This paper presents a complete approach to diagnosing and categorizing diabetic retinopathy using deep learning models. A lightweight Convolutional Neural Network (CNN) is used to detect diabetic retinopathy in fundus images. This CNN has been developed to have fewer parameters and calculations, making it suited for resource-constrained environments while retaining decent performance. The categorization of diabetic retinopathy is carried out with the help of EfficientNet. This model uses an innovative compound scaling approach to strike a balance between the model's depth, width, and resolution. As a result, it maximizes computing efficiency while preserving high accuracy. The proposed detection model obtained an accuracy of 95%, and the classification model produced an accuracy of 84%.
Downloads
References
Gadekallu, Thippa Reddy, Neelu Khare, Sweta Bhattacharya, Saurabh Singh, Praveen Kumar Reddy Maddikunta, and Gautam Srivastava. "Deep neural networks to predict diabetic retinopathy."
Islam, Md Robiul, Lway Faisal Abdulrazak, Md Nahiduzzaman, Md Omaer Faruq Goni, Md Shamim Anower, Mominul Ahsan, Julfikar Haider, and Marcin Kowalski. "Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images."
Da Rocha, Douglas Abreu, Flávia Magalhães Freitas Ferreira, and Zelia Myriam Assis Peixoto. "Diabetic retinopathy classification using VGG16 neural network."
Sugeno, Ayaka, Yasuyuki Ishikawa, Toshio Ohshima, and Rieko Muramatsu. "Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning."
Riaz, Hamza, Jisu Park, Hojong Choi, Hyunchul Kim, and Jungsuk Kim. "Deep and densely connected networks for classification of diabetic retinopathy." Diagnostics 10, no. 1 (2020): 24.
Vinayaki, V. Desika, and R. J. N. P. L. Kalaiselvi. "Multithreshold image segmentation technique using remora optimization algorithm for diabetic retinopathy detection from fundus images." Neural Processing Letters 54, no. 3 (2022):
Chu, Aan, David Squirrell, Andelka M. Phillips, and Ehsan Vaghefi. "Essentials of a robust deep learning system for diabetic retinopathy screening: a systematic literature review."
Rahim, Sarni Suhaila, Vasile Palade, Ibrahim Almakky, and Andreas Holzinger. "Detection of diabetic retinopathy and maculopathy in eye fundus images using deep learning and image augmentation." In Machine Learning and Knowledge Extraction: Third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2019, Canterbury, UK, August 26–29, 2019, Proceedings 3, pp. 114-127. Springer International Publishing, 2019.
Khan, Zubair, Fiaz Gul Khan, Ahmad Khan, Zia Ur Rehman, Sajid Shah, Sehrish Qummar, Farman Ali, and Sangheon Pack. "Diabetic retinopathy detection using VGG-NIN a deep learning architecture." IEEE Access 9 (2021): 61408-61416.
Tsiknakis, Nikos, Dimitris Theodoropoulos, Georgios Manikis, Emmanouil Ktistakis, Ourania Boutsora, Alexa Berto, Fabio Scarpa, Alberto Scarpa, Dimitrios I. Fotiadis, and Kostas Marias. "Deep learning for diabetic retinopathy detection and classification based on fundus images: A review." Computers in biology and medicine 135 (2021): 104599.
Saini, Manisha, and Seba Susan. "Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets." Computers in Biology and Medicine 149 (2022): 105989.
Hacisoftaoglu, Recep E., Mahmut Karakaya, and Ahmed B. Sallam. "Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems." Pattern recognition letters 135 (2020): 409-417.
Qureshi, Imran, Jun Ma, and Qaisar Abbas. "Diabetic retinopathy detection and stage classification in eye fundus images using active deep learning." Multimedia Tools and Applications 80 (2021): 11691-11721.
Li, Xuechen, Linlin Shen, Meixiao Shen, Fan Tan, and Connor S. Qiu. "Deep learning based early-stage diabetic retinopathy detection using optical coherence tomography." Neurocomputing 369 (2019).
Atwany, Mohammad Z., Abdulwahab H. Sahyoun, and Mohammad Yaqub. "Deep learning techniques for diabetic retinopathy classification: A survey." IEEE Access 10 (2022):
Gayathri, S., Varun P. Gopi, and P. Palanisamy. "Diabetic retinopathy classification based on multipath CNN and machine learning classifiers." Physical and engineering sciences in medicine 44, no. 3 (2021): 639-653.
Bodapati, Jyostna Devi, Nagur Shareef Shaik, and VeeranjaneyuluNaralasetti. "Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification." Journal of Ambient Intelligence and Humanized Computing 12, no. 10 (2021).
Adriman, Ramzi, Kahlil Muchtar, and Novi Maulina. "Performance evaluation of binary classification of diabetic retinopathy through deep learning techniques using texture feature." Procedia Computer Science 179 (2021).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 D. Praneeth, N. Satheesh Kumar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
