COVID-19 Radiography Using ConvNets

The COVID-19 pandemic continues to have a negative impact on the fitness and well being of the worldwide population. A vital step in tackling the COVID-19 is a successful screening of patients, with one of the key screening approaches being radiological imaging using chest radiography. This study aims to automatically identify patients with COVID-19 pneumonia using digital x-ray images of the chest while increasing the accuracy of the diagnosis using Convolution Neural networks (CNN). The data-set consists of 5380 X-ray images consisting of 1345 X-ray images each of COVID patients, Lung Opacity, Normal patients and Viral Pneumonia. In this study, CNN based model have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiography and gives a classification accuracy of 93.77% (training accuracy of 99.81% and validation accuracy of 95.45%). © 2022 IEEE.

Comparative Analysis of Deep-Learning techniques for Depressive Text Classification

Psychiatric problems and disorders are an epidemic in their own right, however, they often go unnoticed & undetected. Post COVID-19, given that most families & individuals were forced to isolate themselves in their homes for huge periods of time, it only made things worse. We humans being social animals needed a refuge, therefore the volume of interactions & personal posts on social media platforms skyrocketed. Whilst, there's huge leverage of text classification techniques using Deep Learning algorithms for financial, commercial applications eg. stock market news analysis, analysing customer behavior, etc. but similar applications in the field of Mental health are quite meager. The text on the social media feed of a person can be critical and of huge help in expeditious detection of depressive disorders. Via the medium of this paper, our aim is to find an optimum solution for the above-addressed problem, we take the real-time user data from an online social networking platform, after which it is pre-processed and analysed, thereafter we use this data to build deep learning-based classifier models i.e. LSTM, (CNN+LSTM), GRU, these models are improved using optimisation algorithms, furthermore, these models are compared and analysed to check which text classification algorithm works best for our use case. © 2022 IEEE.