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.
Highlights
Diabetes mellitus (DM) is a chronic metabolic problem that can severely affect various human organs [1,2], including eyes, due to vision-threatening Diabetic retinopathy (DR) [3,4]
The hidden decision-making process of the convolutional neural network (CNN) was shown by computing Grad-CAM while diagnosing COVID-19 from chest images [61], which is useful to solve the current task
Training a CNN model from scratch or fine-tuning it through transfer learning is challenging due to the un- availability of large datasets. These issues are addressed by modifying the architecture of a pre-trained CNN model
Summary
Diabetes mellitus (DM) is a chronic metabolic problem that can severely affect various human organs [1,2], including eyes, due to vision-threatening DR [3,4]. DR is a leading cause of preventable blindness among working-age adults [3,7,8,9]. It is usually asymptomatic in its early stage [10], and late identification leads to a substantial loss of vision [11,12]. Small hemorrhages are of varying size and appear similar to MAs. Small HEs are usually known as “small red dots”. These spots are referred to as red lesions of DR and are abbreviated as HMAs collectively [3,13,14,15,16,17]
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