Improving Automated Detection of Cataract Disease through Transfer Learning using ResNet50

Manual diagnosis of eye diseases through ocular fundus scans is a challenging and complicated task because it is time-consuming and prone to errors. Deep learning techniques are used to detect various ocular diseases from fundus images. Such techniques can accurately classify ocular scans, enabling automated and precise detection of ocular diseases. This study uses the ResNet50 transfer learning model, data augmentation, fine-tuning, binary classification, and rigorous evaluation to achieve state-of-the-art results in the detection of cataract eye disease. This study was primarily implemented on a heavily skewed ODIR-5K dataset comprising 5000 fundus images. These ocular images are distributed unevenly among eight disease classes, including cataract, glaucoma, diabetic retinopathy, age-related macular degeneration, and others. In response to this imbalance and disparity, the proposed approach involved converting the multiclass problem into binary classification tasks, maintaining an equitable distribution of samples within each class. A balanced dataset was used to train a binary classifier using the ResNet50 CNN model. The system achieved an overall test accuracy of 96.63%, outperforming previous methods in differentiating between normal and cataract cases. In general, achieving dataset balance and employing the ResNet50 model enhances the accuracy of automated diagnosis of ocular diseases based on fundus images.