Version 1
: Received: 1 March 2024 / Approved: 3 March 2024 / Online: 4 March 2024 (09:50:30 CET)
How to cite:
Mutawa, A.; Al-Sabti, K.; Raizada, S.; Sruthi, S. A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform. Preprints2024, 2024030092. https://doi.org/10.20944/preprints202403.0092.v1
Mutawa, A.; Al-Sabti, K.; Raizada, S.; Sruthi, S. A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform. Preprints 2024, 2024030092. https://doi.org/10.20944/preprints202403.0092.v1
Mutawa, A.; Al-Sabti, K.; Raizada, S.; Sruthi, S. A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform. Preprints2024, 2024030092. https://doi.org/10.20944/preprints202403.0092.v1
APA Style
Mutawa, A., Al-Sabti, K., Raizada, S., & Sruthi, S. (2024). A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform. Preprints. https://doi.org/10.20944/preprints202403.0092.v1
Chicago/Turabian Style
Mutawa, A., Seemant Raizada and Sai Sruthi. 2024 "A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform" Preprints. https://doi.org/10.20944/preprints202403.0092.v1
Abstract
Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetic people. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today's growing medical field poses more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model's capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image undergoes preprocessing using contrast-limited adaptive histogram equalization (CLAHE) and the discrete wavelet transform (DWT). The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy (DDR) and the Asia Pacific Tele-Ophthalmology Society (APTOS). The CNN model is tuned with different learning rates and optimizers. The CNN model with Adam optimizer achieved 70% accuracy and an area under curve (AUC) score of 0.82. The recommended study results may reduce diabetes-related vision impairment by early DR severity identification.
Keywords
artificial intelligence; convolutional neural networks; diabetic retinopathy; deep learning; medical image analysis
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
