Tejashwini N,
Shantakumar B Patil,
Rabbani S,
Shreyas T.S,
Sumanth V,
Darshan R,
- Associate Professor Department. of Computer Science, Sai Vidya Institute of Technology Bengaluru India
- HOD Professor Department. of Computer Science, Sai Vidya Institute of Technology Bengaluru India
- Student Department. of Computer Science, Sai Vidya Institute of Technology Bengaluru India
- Student Department. of Computer Science, Sai Vidya Institute of Technology Bengaluru India
- Student Department. of Computer Science, Sai Vidya Institute of Technology Bengaluru India
- Student Department. of Computer Science, Sai Vidya Institute of Technology Bengaluru India
Abstract
This project delves into the evaluation of three prominent deep learning architectures Basic CNN, ResNet, and DenseNet for their efficacy in detecting diabetic retinopathy from retinal images. Utilizing a diverse dataset, the study employs standard deep learning frameworks to train and validate each model. The focus extends to exploring the potential benefits of transfer learning on a limited dataset. Evaluation metrics like specificity, sensitivity, and accuracy are employed for a comprehensive a comparison study of the models. The results and ensuing discussion reveal nuanced performance characteristics, highlighting potential tradeoffs between accuracy and computational demands. This research contributes valuable insights to the field of medical imaging and analysis, aiding in the selection of optimal models for the precise detection of diabetic retinopathy. The findings are highly significant for healthcare professionals and researchers as they provide valuable insights that can guide future efforts in enhancing and refining deep learning models specifically designed for the diagnosis of diabetic retinopathy. This advancement has the potential to improve early detection, treatment, and overall management of the condition, ultimately benefiting patients and contributing to the field of medical research
Keywords: Diabetic retinopathy, Fundus images, Convolutional Neural Architecture. sensitivity, accuracy, medical imaging, model evaluation, computational demands, healthcare, early detection, treatment, medical research
[This article belongs to Research & Reviews : A Journal of Medical Science and Technology(rrjomst)]
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Research & Reviews : A Journal of Medical Science and Technology
| Volume | 13 |
| Issue | 02 |
| Received | May 1, 2024 |
| Accepted | June 24, 2024 |
| Published | August 13, 2024 |
