Abstract
The Covid-19 disease has spread widely over the whole world since the
beginning of 2020. Following the epidemic which started in Wuhan, China
on January 30, 2020 the World Health Organization (WHO) declared a
global health emergency and a pandemic. Researchers of different
disciplines work along with public health officials to understand the
SARS-CoV-2 pathogenesis and jointly with the policymakers urgently
develop strategies to control the spread of this new disease. Recent
findings have observed specific image patterns from computed tomography
(CT) for patients infected by SARS-CoV-2 which are distinct from the
other pulmonary diseases. In this paper, we propose an
explainable-by-design that has an integrated image segmentation
mechanism based on SLIC that improves the algorithm performance and the
interpretability of the resulting model. In order to evaluate the
proposed approach, we used the SARS-CoV-2 CT scan dataset that we
published recently and has been widely used in the literature. The
proposed Super-xDNN could obtain statistically better results than
traditional deep learning approaches as DenseNet-201 and Resnet-152.
Furthermore, it also improved the explainability and interpretability of
its decision mechanism when compared with the xDNN basis approach that
uses the whole image as prototype. The segmentation mechanism of
Super-xDNN favored a decision structure that is more close to the human
logic. Moreover, it also allowed the provision of new insights as a
heat-map which highlights the areas with highest similarities with
Covid-19 prototypes, and an estimation of the area affected by the
disease.