ABSTRACT
Most of the countries in the world are now fighting against Covid-19 and many of the people are losing their life because of the less immunity or due to the late diagnostics and it is especially in the case of old age people and people with other medical issues. The concept of early detection of disease is really important in the case of the Covid-19 scenario because along with the infected people, the other people who are in close contact with the infected persons will also have life risk. During this pandemic, pneumonia and Covid-19 people suffers from almost the same symptoms. So, the proposed work designs an automated system that can perform multi-classification on general health, pneumonia and Covid-19 through Chest X-Rays by designing an optimized auto encoder- decoder network. Most of the earlier approaches which are used to perform the binary classification couldn't differentiate the Covid-19 and Pneumonia effectively because the traditional CNN extract the high level features, which are similar in case of COVID-19 & Pneumonia. These two have variations in the case of low level features. The major focus of this paper is to construct a hyper-parameterized auto encoder-decoder system that can help the user to detect level of lung infection. The level of infection helps the model to accurately classify the model. This method helps doctors and other medical-related people with the early diagnosis of disease. © 2022 Little Lion Scientific.
ABSTRACT
In the recent past, mental health has become a global concern. COVID-19 has further caused a rapid surge in depression. Depression is a serious mental illness that is impacting the lives of individuals of all ages all around the world. Depression affects a person's physiological well-being as well as their emotional state. Now days, Depression is the most common element experienced by the human beings irrespective of their age factor and professional life. To detect the depression status among the persons, the system uses different approaches by using the sensor technology. The automatic identification of depression at early stages or immediately helps the clinical studies to cure the people accurately. In this proposed research, the system aims to identify the depression using facial expressions, voice, live video capturing, by analysing their tweets, status, posts in the social media. By applying computer vision integrated with ML and DL techniques, the entire capturing and analysis process gets automated and the complexity involved in the model designing gets reduced because the system focuses more on extracting the statistical features involved in movements and behaviour of the human being. Most of the existing research works focuses on the unimodal development which focuses on the single component analysis but the proposed research aims to focus on the multi modal with a fusion of different modalities of learning approaches involved in detection of depression, this survey provides an overview of numerous methodologies that have been created with the goal of employing emotion recognition to analyse depression.