A Comprehensive Review of various Deep Learning Techniques for SARS-COVID-19 Detection

Namrata Nikam, Sanjay Ganorkar

Abstract


Abstract— The COVID-19 pandemic continues to set alarming records in terms of both cumulative and daily infection numbers on a global scale. This unprecedented healthcare crisis necessitates accurate and swift methods for detecting COVID-19 cases through the analysis of patient data. Deep learning (DL) methods have demonstrated their utility in rapidly and effectively identifying and outlining infectious areas within radiological images. The research aims to provide a comprehensive overview of innovative deep learning-based applications in the context of medical imaging modalities, specifically computer tomography (CT), for the detection and classification of COVID-19. Initially, we will establish a taxonomy of medical imaging and offer a concise overview of various types of deep learning (DL) methodologies. Subsequently, we will utilize deep learning techniques to provide an overview of systems developed for the purpose of detecting and classifying COVID-19. Additionally, we will provide an overview of the most commonly employed databases for training these networks. Lastly, we will delve into the challenges associated with the implementation of deep learning algorithms in COVID-19 detection and explore potential avenues for future research in this field.

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