Coronavirus-2019 disease (Covid-19) is a contagious respiratory disease that emerged in late 2019 and has been recognized by the World Health Organization (WHO) as a global pandemic in early 2020. Since then, researchers have been exploring various strategies and techniques to fight against this outbreak. The point when the pandemic appeared was also a period in which Machine Learning (ML) and Deep Learning (DL) algorithms were competing with traditional technologies, leading to significant findings in diverse domains. Consequently, many researchers employed ML/DL to speed up Covid-19 detection, prevention, and treatment. This paper reviews the state-of-the-art ML/DL tools used, thoroughly evaluating these techniques and their impact on the battle against Covid-19. This article aims to provide valuable insight to the researchers to assess the use of ML against the Covid-19 pandemic.
Microwave Imaging is a prominent technique in current imaging modalities for breast cancer detection and its treatment, primarily due to the significant dielectric property contrast between normal and malignant breast tissues. This study describes how VORD Viewpoint-Oriented Requirement Definition (VORD) method is used to examine patients for early detection of cancer by microwave imaging by realizing the existence of multiple perspectives by terminating differences in the demands of stakeholders. The proposed system in this paper is based on microwave imaging by Indirect Holography for Breast Cancer Detection System (IH-BCDS) using Viewpoint-Oriented Requirement Definition (VORD) method. The suggested approach for developing such system is to investigate the possibility of detecting tumors at an early stage by utilizing VORD as analysis tool which is an activity oriented framework for IH-BCDS requirement elicitation. If the system gets implemented successfully, it can play a vital role in detecting the tumor at an early stage by helping a medical specialist in decision making to diagnose the patient.
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