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Automated diabetic retinopathy severity grading using novel DR-ResNet + deep learning model

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Abstract

Diabetic retinopathy, a microvascular condition associated with an increased risk of cardiovascular disease, poses a substantial global healthcare challenge. The demand for timely diagnosis has prompted the development of automated solutions due to the scarcity of specialists. In this paper, we introduce a ground-breaking approach to diabetic retinopathy detection – the Diabetic Retinopathy Residual Network (DR-ResNet +). The proposed model leverages the power of deep learning to automatically extract features, achieving optimal results in just seven training epochs. The DR-ResNet + architecture is meticulously designed by incorporating a series of convolutional, pooling, and fully connected layers. Hyperparameter optimization is done using both grid and random search techniques to ensure peak performance. To validate the proposed model’s robustness, simulated results are compared with well-established deep learning models, such as GoogleNet, VGG16, and AlexNet, using a comprehensive Kaggle dataset comprising over 35,000 retinal images. Moreover, the proposed model is also tested on external datasets like MESSIDOR and IDRiD for its validation. Simulation results reveal that the proposed DR-ResNet + model not only reduces training time by an impressive 95% but also exhibits outstanding performance metrics, including an accuracy of 0.9898, specificity of 0.9916, precision of 0.9670, sensitivity of 0.9829, and an F1-score of 0.9748. These findings position the proposed model as exceptionally well-suited for real-time clinical applications, offering a potential game-changer in diabetic retinopathy diagnosis. This paper presents DR-ResNet + as a pioneering advancement in diabetic retinopathy diagnosis. With its rapid training, superior accuracy, and significant real-world implications, the model holds promise for transforming the landscape of healthcare by providing timely and precise diagnoses for this critical condition.

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Authors and Affiliations

  1. SEEE, Lovely Professional University, Phagwara, Punjab, 144411, India

    Samiya Majid Baba & Indu Bala

  2. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

    Gaurav Dhiman

  3. Centre of Research Impact and Outreach, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Gaurav Dhiman

  4. Division of Research and Development, Lovely Professional University, Phagwara, India

    Gaurav Dhiman

  5. MEU Research Unit, Middle East University, Amman, Jordan

    Gaurav Dhiman

  6. Southern Federal University, Rostov-on-Don, Russia

    Ashutosh Sharma

  7. Business School, Henan University of Science and Technology, Henan, China

    Ashutosh Sharma

  8. Business Information Technology Division, Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University, Bangkok, Thailand

    Wattana Viriyasitavat

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  1. Samiya Majid Baba
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Baba, S.M., Bala, I., Dhiman, G. et al. Automated diabetic retinopathy severity grading using novel DR-ResNet + deep learning model. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18434-2

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