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Explainable COVID-19 Classification Via Variational and Perceptual Autoencoder-Guided Occlusion

Year: 2026
Type: article
Abstract: Technology played a crucial role in combating the COVID pandemic, both in the rapid development of vaccines and the early detection of the virus. Consequently, numerous studies in the medical field have focused on leveraging the power of artificial intelligence for COVID-19 detection. However, in the medical domain, it is essential to have a clear understanding of the processes and algorithms used in decision-making, as these directly impact people’s health. Therefore, efforts have been made to implement explainable artificial intelligence techniques, enabling humans to understand and explain the deep learning algorithms used in disease detection. In this work, we present an approach to detecting COVID-19 in chest X-rays that combines reconstruction-based anomaly discov- ery with perturbation-based attribution. Specifically, we use a Variational Autoencoder trained on healthy lungs to identify lung anomalies, and we additionally evaluate a Per- ceptual Autoencoder (PAE) that incorporates a perceptual loss to produce sharper recon- structions and more contrastive residual maps. These signals are then used to highlight critical image evidence through both patch-level scoring (grid-based occlusion) and pixel- level masking derived from reconstruction-error maps, enabling healthcare professionals to localize relevant regions more effectively. Moreover, the proposed framework provides clearer explanations of the model’s decisions by quantifying the prediction change after occluding the identified regions, reducing the complexity of the “black boxes” generated by deep learning neural networks. With this methodology, we aim to improve the effec- tiveness and reliability of early COVID-19 detection through chest X-rays.
DOI: 10.19153/cleiej.29.3.6
Language: English
Field: Medicine
Open Access status: diamond
OpenAlex ID: W7168249378
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