The objective of this research is to develop a convolutional neural network model ‘COVID‐Screen‐Net’ for multi‐class classification of chest X‐ray images into three classes viz. COVID‐19, bacterial pneumonia, and normal. The model performs the automatic feature extraction from X‐ray images and accurately identifies the features responsible for distinguishing the X‐ray images of different classes. It plots these features on the GradCam. The authors optimized the number of convolution and activation layers according to the size of the dataset. They also fine‐tuned the hyperparameters to minimize the computation time and to enhance the efficiency of the model. The performance of the model has been evaluated on the anonymous chest X‐ray images collected from hospitals and the dataset available on the web. The model attains an average accuracy of 97.71% and a maximum recall of 100%. The comparative analysis shows that the ‘COVID‐Screen‐Net’ outperforms the existing systems for screening of COVID‐19. The effectiveness of the model is validated by the radiology experts on the real‐time dataset. Therefore, it may prove a useful tool for quick and low‐cost mass screening of patients of COVID‐19. This tool may reduce the burden on health experts in the present situation of the Global Pandemic. The copyright of this tool is registered in the names of authors under the laws of Intellectual Property Rights in India with the registration number ‘SW‐13625/2020’.

Amidst the global pandemic and catastrophe created by 'COVID-19', every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding the degree of genomic similarity among the Severe Acute Respiratory Syndrome-Coronavirus 2 and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome-Coronavirus, Human Immunodeficiency Virus, and Human T-cell Leukaemia Virus. The experimental results on the datasets available at National Centre for Biotechnology Information, GitHub, and Kaggle repositories show that it is successful in detecting the genome of 'SARS-CoV-2' in the host genome with an accuracy of 99.27% and screening of chest radiographs into COVID-19, non-COVID pneumonia and healthy with a sensitivity of 95.47%. Thus, it may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of 'COVID-19'.

Amidst the global pandemic and catastrophe created by ‘COVID-19’, every research institution and scientists are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning model for finding the degree of similarity of the genome of the Severe Acute Respiratory Syndrome-Coronavirus 2 (‘SARS-CoV-2’) with a given genome. This research also aims at detecting the genome of ‘SARS-CoV-2’ in the host human beings. The experimental results on the dataset publicly available at National Centre for Biotechnology Information, show that the model is effective in predicting the similarity score of the genomic sequence of ‘SARS-CoV-2’ and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome Coronavirus, Human Immunodeficiency Virus, and Human T- cell Leukaemia Virus. This is successful in detecting the genome of ‘SARS-CoV-2’ in the host genome with an accuracy of 99.27%. It may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of ‘COVID-19’.

No abstract

The increase in use of ICT tools and decrease in physical activities has increased the risk of disorders such as diabetes, hypertension, myopia, hypermetropia, etc. These disorders make the person more prone to eye disease such as glaucoma. The actual causes of glaucoma are still unknown. But the study of medical literature reveals that the factors such as intraocular pressure, thyroid, diabetics, eye injuries, eye surgeries, ethnic background, and myopia makes the person more prone to glaucoma. The difficulty in early detection make it an invisible thief of sight. Therefore, it is the demand of the day to design a system for its early detection. The aim of this chapter is to develop a convolutional neural network model “GlaucomaDetector” for detection of glaucoma at an early stage. The evaluation of the model on the publicly available dataset reports the accuracy of 99% for prediction of glaucoma from the input images of retina. This may prove a useful tool for doctors for quick prediction of glaucoma at an early stage. Thus, it can minimize the risk of blindness in patients.