Towards Explainable AI on Chest X-Ray Diagnosis Using Image Segmentation and CAM Visualization

Abstract

Computer vision in medical diagnosis has achieved a high level of success in diagnosing diseases with high accuracy. However, conventional classifiers that produce an image-to-label result provide insufficient information for medical professionals to judge and raise concerns over the trust and reliability of a model with results that cannot be explained. Class activation maps are a method of providing insight into a convolutional neural network’s feature maps that lead to its classification but in the case of lung diseases, the region of concern is only the lungs. Therefore, the proposed model combines image segmentation models and classifiers to crop out only the lung region of a chest X-ray’s class activation map to provide a visualization that improves the explainability and trust of an AI’s diagnosis by focusing on a model’s weights within the region of concern. The proposed U-Net model achieves 97.72% accuracy and a dice coefficient of 0.9691 on testing data from the COVID-QU-Ex Dataset which includes both diseased and healthy lungs.