Diabetic retinopathy stage detection using convolutional fine-tuned transfer Learning model

Jahnabi Medhi; Mithun Karmakar; Anup Kumar Barman; Subhash Mondal; Amitava Nag

doi:10.52756/10.52756/ijerr.2023.v31spl.004

Authors

Jahnabi Medhi Department of Computer Science and Engineering, Central Institute of Technology Kokrajhar, Kokrajhar, Assam, India https://orcid.org/0009-0005-7986-0410
Mithun Karmakar Department of Computer Science and Engineering, Central Institute of Technology Kokrajhar, Kokrajhar, Assam, India https://orcid.org/0000-0003-0683-8800
Anup Kumar Barman Department of Computer Science and Engineering, Central Institute of Technology Kokrajhar, Kokrajhar, Assam, India https://orcid.org/0000-0002-5697-0431
Subhash Mondal Department of Computer Science and Engineering, Central Institute of Technology Kokrajhar, Kokrajhar, Assam, India https://orcid.org/0000-0002-4203-8467
Amitava Nag Department of Computer Science and Engineering, Central Institute of Technology Kokrajhar, Kokrajhar, Assam, India https://orcid.org/0000-0003-4408-7307

DOI:

https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.004

Keywords:

Deep Learning, Deep Convolutional Neural Network (DCNN), Diabetic Retinopathy (DR), Image Classification, Retinal Fundus images

Abstract

Diabetic Retinopathy (DR) is a prevalent eye condition that occurs as a frequent complication among individuals with diabetes, particularly those who have been living with the disease for an extended period of time. This study uses fundus images to diagnose DR at five stages from early to late with No DR, Mild, Moderate, Severe, and Proliferative DR, commonly known as Stage 0 to Stage 4, respectively. This will aid in the timely treatment of diabetic patients preventing them from developing DR as early as possible. We used two most popular open-source datasets, the DR Detection database, namely APTOS 2019 and EyePACS, and combined them to create a larger dataset to trade off the data-centric obstacle and shortfall for any Deep Learning-based prediction models. Data augmentation and preprocessing techniques are applied to the images before feeding them to the proposed model to get a more accurate and efficient one. In the modern age oriented to Artificial Intelligence (AI), it is necessary to thoroughly analyze the identification of DR based on the existing Deep Learning (DL) models. After learning about the limitations of existing models, we have fine-tuned the ResNet50, DenseNet201 and InceptionV3 to enhance the model performance of the detection and categorization of DR. We have since proposed three Deep Convolutional Neural Networks (DCNN) models with better outcome based on accuracy than the existing state-of-the-art (SOTA) models. The fine-tuned DenseNet201 model, among the other two, performed significantly better with a validation accuracy of 90.04% and a negligible amount of loss, irrespective of each class, under the best configurable test conditions.

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