Ocular Disease Detection Using Fundus Images: A Hybrid Approach of Grad-CAM and Multiscale Retinex Preprocessing With VGG16 Deep Features and Fine KNN Classification - Shreemat Kumar Dash; Kante Satyanarayana; Santi Kumari Behera; Sudarson Jena; Ashoka Kumar Ratha; Prabira Kumar Sethy; Aziz Nanthaamornphong (Applied Computational Intelligence and Soft Computing)
The emergence of deep learning has markedly enhanced the identification and diagnosis of ocular diseases, providing considerable benefits compared to conventional machine learning techniques. This research investigates the application of deep feature extraction for classifying eight different ocular diseases. The VGG16, a pretrained convolutional neural network (CNN) model, was employed for feature extraction, while the fine k-nearest neighbor (KNN) classifier was utilized for classification. Experimental results showed an initial classification accuracy of 89.2% using features from Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps and 83.1% using Multiscale Retinex (MSR) enhanced images. However, combining both feature sets led to an improved classification accuracy of 96.5%. Despite these promising results, several challenges remain, including the need for models that generalize across diverse patient imaging and demographics modalities, the accessibility of extensive annotated datasets, and the interpretability of models. Ethical issues and legal frameworks are also crucial for the safe and fair implementation of AI in medical services. The study suggests that future efforts in deep learning for ophthalmology should focus on creating large-scale, annotated datasets to enhance the detection of ocular diseases.