Coronavirus covid-19 detection by means of explainable deep learning

Abstract

The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95.

Graph data science and machine learning for the detection of COVID-19 infection from symptoms

“… Mercaldo et al (2023) applied an automatic and rapid detection of COVID-19 infection based on 18,000 lung CT scans for 45 patients. They relied on deep learning to distinguish between COVID-19 infection, other pulmonary infection, and healthy patients.…”

Deep learning‐based COVID‐19 diagnosis using CT scans with laboratory and physiological parameters

“…Mercaldo et al. [44] designed and executed a deep learning‐based CNN approach to detect lung diseases using CT scan images. The suggested method is to determine whether CT scan images are associated with a healthy patient, a patient with a pulmonary condition, or a patient affected by COVID‐19.…”

A survey on deep learning models for detection of COVID-19

“…The authors claimed this research to be the first of its kind in which 3 types of lung diseases are considered as the model outputs. An explainable COVID-19 detection model based on VGG-16 and Grad-CAM techniques was proposed in [ 34 ] which achieved an accuracy of 95%.…”