ABSTRACT
Air pollution is a menacing issue in this current era. The atmosphere is composed of unwanted, non-efficient respiratory particles that are harmful to humans. The air people breathe is full of harmful contaminated particles resulting in many non-infectious respiratory diseases. The world-threatening COVID-19 pandemic has led the people to care about their personal hygiene and a mask is turning out to be an eminent requirement for every human to prevent the first stage of attack by the deadly coronavirus. Indeed, asthma is turning worse amidst humans, mostly because of these toxic and life-wrenching allergens that coexist in the atmosphere. To realize a knowledge-based sample a design of smart mask is proposed in the paper. © 2020 IEEE.
ABSTRACT
Covid-19, an infectious disease, is currently the leading topic of conversation throughout the world. Declared as a pandemic by the WHO, the virus attacks the respiratory system and causes dry cough, fever and in severe cases difficulty in breathing. In this paper, we analyse the similarity in features between the novel coronavirus 2019 and various other lung diseases such as Pneumonia, Pneumothorax, Atelectasis, Pleural Thickening etc. Chest X-ray scans in the posteroanterior view for various diseases are collected. Convolutional Neural Network using the Residual Network (ResNet) is built to identify the similar regions in the chest X-rays of COVID-19 and various lung diseases. The regions of similarity are visualized using class activation maps. A total of eleven conditions affecting the lungs are studied and compared to COVID-19. The results show that Atelectasis, Consolidation, Emphysema, and Pneumonia are most similar in nature to COVID-19 of the eleven diseases considered. Diseases which our model detects as similar to COVID-19, occur either prior to onset of COVID-19 or as a consequence of COVID-19. © 2020 IEEE.