MCSC-Net: COVID-19 detection using deep-Q-neural network classification with RFNN-based hybrid whale optimization.

BACKGROUND: COVID-19 is the most dangerous virus, and its accurate diagnosis saves lives and slows its spread. However, COVID-19 diagnosis takes time and requires trained professionals. Therefore, developing a deep learning (DL) model on low-radiated imaging modalities like chest X-rays (CXRs) is needed. OBJECTIVE: The existing DL models failed to diagnose COVID-19 and other lung diseases accurately. This study implements a multi-class CXR segmentation and classification network (MCSC-Net) to detect COVID-19 using CXR images. METHODS: Initially, a hybrid median bilateral filter (HMBF) is applied to CXR images to reduce image noise and enhance the COVID-19 infected regions. Then, a skip connection-based residual network-50 (SC-ResNet50) is used to segment (localize) COVID-19 regions. The features from CXRs are further extracted using a robust feature neural network (RFNN). Since the initial features contain joint COVID-19, normal, pneumonia bacterial, and viral properties, the conventional methods fail to separate the class of each disease-based feature. To extract the distinct features of each class, RFNN includes a disease-specific feature separate attention mechanism (DSFSAM). Furthermore, the hunting nature of the Hybrid whale optimization algorithm (HWOA) is used to select the best features in each class. Finally, the deep-Q-neural network (DQNN) classifies CXRs into multiple disease classes. RESULTS: The proposed MCSC-Net shows the enhanced accuracy of 99.09% for 2-class, 99.16% for 3-class, and 99.25% for 4-class classification of CXR images compared to other state-of-art approaches. CONCLUSION: The proposed MCSC-Net enables to conduct multi-class segmentation and classification tasks applying to CXR images with high accuracy. Thus, together with gold-standard clinical and laboratory tests, this new method is promising to be used in future clinical practice to evaluate patients.

Analyzing Indian general public's perspective on anxiety, stress and trauma during Covid-19 - A machine learning study of 840,000 tweets.

BACKGROUND AND AIMS: Ever since COVID-19 was declared a pandemic by WHO in late March 2020, more and more people began to share their opinions online about the anxiety, stress, and trauma they suffered because of the pandemic. However, very few studies were conducted to analyze the general public's perception of what causes stress, anxiety, and trauma during COVID-19. This study focuses particularly on understanding Indian citizens. METHODS: By using Machine learning techniques, particularly Natural language processing, this study focuses on understanding the attitude of Indian citizens while discussing the anxiety, stress, and trauma created because of COVID-19 and the major reasons that cause it. We used Tweets as data for this study. We have used 840,000 tweets for this study. RESULTS: Our sentiment analysis study revealed the interesting fact that, even while discussing about the stress, anxiety, and trauma caused by COVID-19, most of the tweets were in neutral sentiments. Death and Lockdown caused by the COVID-19 were the two most important aspects that cause stress, anxiety, and Trauma among Indian citizens. CONCLUSION: It is important for policymakers and health professionals to understand common citizen's perspectives of what causes them stress, anxiety, and trauma to formulate policies and treat the patients. Our study shows that Indian citizens use social media to share their opinions about COVID-19 and as a coping mechanism in unprecedented time.

Analyzing the attitude of Indian citizens towards COVID-19 vaccine - A text analytics study.

BACKGROUND AND AIMS: The government of India recently planned to start the process of the mass vaccination program to end the COVID-19 crises. However, the process of vaccination was not made mandatory, and there are a lot of aspects that arise skepticism in the minds of common people regarding COVID-19 vaccines. This study using machine learning techniques analyzes the major concerns Indian citizens voice out about COVID-19 vaccines in social media. METHODS: For this study, we have used social media posts as data. Using Python, we have scrapped the social media posts of Indian citizens discussing about the COVID- 19 vaccine. In Study 1, we performed a sentimental analysis to determine how the general perception of Indian citizens regarding the COVID-19 vaccine changes over different months of COVID-19 crises. In Study 2, we have performed topic modeling to understand the major issues that concern the general public regarding the COVID- 19 vaccine. RESULTS: Our results have indicated that 47% of social media posts discussing vaccines were in a neutral tone, and nearly 17% of the social media posts discussing the COVID-19 vaccine were in a negative tone. Fear of health and allergic reactions towards the vaccine are the two prominent issues that concern Indian citizens regarding the COVID-19 vaccine. CONCLUSION: With the positive sentiments regarding vaccine is just over 35%, the Indian government needs to focus especially on addressing the fear of vaccines before implementing the process of mass vaccination.