Perception and Localization of Macular Degeneration Applying Convolutional Neural Network, ResNet and Grad-CAM

TL;DR

This study employs CNN and ResNet architectures, particularly ResNet50, to classify and localize macular degeneration in fundus images, achieving high accuracy and utilizing Grad-CAM for visualization of affected regions.

Contribution

It introduces a CNN-ResNet based approach for accurate classification and localization of macular degeneration in fundus images, with comprehensive evaluation across different data splits.

Findings

ResNet50 achieved 98.7% training accuracy.

ResNet-based models outperformed simpler CNNs.

Grad-CAM effectively visualized affected regions.

Abstract

A well-known retinal disease that sends blurry visions to the affected patients is Macular Degeneration. This research is based on classifying the healthy and macular degeneration fundus by localizing the affected region of the fundus. A CNN architecture and CNN with ResNet architecture (ResNet50, ResNet50v2, ResNet101, ResNet101v2, ResNet152, ResNet152v2) as the backbone are used to classify the two types of fundus. The data are split into three categories including (a) Training set is 90% and Testing set is 10% (b) Training set is 80% and Testing set is 20%, (c) Training set is 50% and Testing set is 50%. After the training, the best model has been selected from the evaluation metrics. Among the models, CNN with a backbone of ResNet50 performs best which gives the training accuracy of 98.7% for 90% train and 10% test data split. With this model, we have performed the Grad-CAM visualization to get the region of the affected area of the fundus.