Explain COVIDNet: Explainable AI for Transparent and Reliable X-ray Screening
Abstract
Recently, deep learning has emerged as a prominent branch of AI, gaining attention for its high accuracy and wide-ranging application in various domains. The proposed Explain-COVIDNet framework enhances COVID-19 detection from chest X-ray images by addressing key limitations in current deep learning models, including poor interpretability, sensitivity to noise, and limited clinical reliability. It begins with a robust preprocessing stage using a wavelet contrast enhancer, which integrates Discrete Wavelet Transform for denoising and Contrast-Limited Adaptive Histogram Equalization for contrast enhancement. This results in high-quality input images that aid model accuracy. Medical GoogLeNet, a 154-layer architecture with inception modules, is used for deep feature extraction and classification, employing Leaky ReLU to retain subtle but crucial image details. To improve transparency, XGrad-CAM is incorporated for explainability, generating class-specific heatmaps that highlight diagnostically relevant lung regions. The model demonstrates strong performance on two benchmark datasets, achieving up to 95.73% accuracy, 95.72% F1-score, 99.39 AUC, and 91.22 Cohen's Kappa, while maintaining computational efficiency. These results underscore Explain-COVIDNet's potential as a reliable, interpretable, and clinically meaningful tool for automated COVID-19 diagnosis.
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