Augmenting Diabetic Retinopathy Severity Prediction with a Dual-Level Deep Learning Approach Utilizing Customized MobileNet Feature Embeddings
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2023, Vol 2, Issue 4
Abstract
Diabetic retinopathy, a severe ocular disease correlated with elevated blood glucose levels in diabetic patients, carries a significant risk of visual impairment. The essentiality of its timely and precise severity classification is underscored for effective therapeutic intervention. Deep learning methodologies have been shown to yield encouraging results in the detection and categorisation of severity levels of diabetic retinopathy. This study proposes a dual-level approach, wherein the MobileNetV2 model is modified for a regression task, predicting retinopathy severity levels and subsequently fine-tuned on fundus images. The refined MobileNetV2 model is then utilised for learning feature embeddings, and a Support Vector Machine (SVM) classifier is trained for grading retinopathy severity. Upon implementation, this dual-level approach demonstrated remarkable performance, achieving an accuracy rate of 87% and a kappa value of 93.76% when evaluated on the APTOS19 benchmark dataset. Additionally, the efficacy of data augmentation and the handling of class imbalance issues were explored. These findings suggest that the novel dual-level approach provides an efficient and highly effective solution for the detection and classification of diabetic retinopathy severity levels.
Authors and Affiliations
Jyostna Devi Bodapati,Rajasekhar Konda
Keywords
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- EP ID EP731894
- DOI https://doi.org/10.56578/ataiml020401
- Views 78
- Downloads 1
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