Deep Red Lesion Classification for Early Screening of Diabetic Retinopathy

Muhammad Nadeem Ashraf, Muhammad Hussain and Zulfiqar Habib
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Muhammad Nadeem Ashraf: Department of Computer Science, COMSATS University Islamabad, Lahore 54700, Pakistan
Muhammad Hussain: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Zulfiqar Habib: Department of Computer Science, COMSATS University Islamabad, Lahore 54700, Pakistan

Mathematics, 2022, vol. 10, issue 5, 1-26

Abstract: Diabetic retinopathy (DR) is an asymptotic and vision-threatening complication among working-age adults. To prevent blindness, a deep convolutional neural network (CNN) based diagnosis can help to classify less-discriminative and small-sized red lesions in early screening of DR patients. However, training deep models with minimal data is a challenging task. Fine-tuning through transfer learning is a useful alternative, but performance degradation, overfitting, and domain adaptation issues further demand architectural amendments to effectively train deep models. Various pre-trained CNNs are fine-tuned on an augmented set of image patches. The best-performing ResNet50 model is modified by introducing reinforced skip connections, a global max-pooling layer, and the sum-of-squared-error loss function. The performance of the modified model (DR-ResNet50) on five public datasets is found to be better than state-of-the-art methods in terms of well-known metrics. The highest scores (0.9851, 0.991, 0.991, 0.991, 0.991, 0.9939, 0.0029, 0.9879, and 0.9879) for sensitivity, specificity, AUC, accuracy, precision, F1-score, false-positive rate, Matthews’s correlation coefficient, and kappa coefficient are obtained within a 95% confidence interval for unseen test instances from e-Ophtha_MA. This high sensitivity and low false-positive rate demonstrate the worth of a proposed framework. It is suitable for early screening due to its performance, simplicity, and robustness.

Keywords: computer-aided diagnosis; diabetic retinopathy; red lesions; convolutional neural networks; deep residual networks; skip connections (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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