Enhanced Deep Learning Explainability for COVID-19 Diagnosis from Chest X-ray Images by Fusing Texture and Shape Features

Houda El Mohamadi, Mohammed El Hassouni

Abstract: In this paper, we propose a novel method of explainable deep learning for COVID-19 detection in Chest X-ray (CXR) images, employing a Texture-Shape approach. The method extracts texture information from the first convolution layer and shape features from the last convolution layer of Deep Neural Networks, namely CNN, VGG16, and ResNet50. To generate the final explainable map, the extracted texture and shape heatmaps are fused using a guided filter-based method. Our approach is versatile and can be adapted to work with classical explainability methods commonly used in the literature. We validate the efficacy of our method on a well-known COVID-19 database comprising CXR images. We conduct extensive experiments to assess its performance against classical explainability methods such as FEM (Feature Extraction Map) and GradCam (Gradientweighted Class Activation Mapping). For evaluation, we calculate metrics such as the average drop and increase in confidence scores. The obtained results demonstrate that fusing texture and shape information leads to significantly improved explainability compared to conventional methods.

External IDs:dblp:conf/wincom/MohamadiH23