We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Deep Red Lesion Classification for Early Screening of Diabetic Retinopathy.
- Authors
Ashraf, Muhammad Nadeem; Hussain, Muhammad; Habib, Zulfiqar
- 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.
- Subjects
DIABETIC retinopathy; MEDICAL screening; COMPUTER-aided diagnosis; CONVOLUTIONAL neural networks; CLASSIFICATION
- Publication
Mathematics (2227-7390), 2022, Vol 10, Issue 5, p686
- ISSN
2227-7390
- Publication type
Article
- DOI
10.3390/math10050686