An Explainable AI Model for Interpretable Lung Disease Classification

Abstract

In this paper, we develop a framework for lung disease identification from chest X-ray images by differentiating the novel coronavirus disease (COVID-19) or other disease-induced lung opacity samples from normal cases. We perform image processing tasks, segmentation, and train a customized Convolutional Neural Network (CNN) that obtains reasonable performance in terms of classification accuracy. To address the black-box nature of this complex classification model, which emerged as a key barrier to applying such Artificial Intelligence (AI)-based methods for automating medical decisions raising skepticism among clinicians, we address the need to quantitatively interpret the performance of our adopted approach using a Layer-wise Relevance Propagation (LRP)-based method. We also used a pixel flipping-based, robust performance metric to evaluate the explainability of our adopted LRP method and compare its performance with other explainable methods, such as Local Interpretable Model Agnostic Explanation (LIME), Guided Backpropagation (GB), and Deep Taylor Decomposition (DTD).

Ähnliche Arbeiten