Covid Prediction and Covid Detection via X-Ray Images

The situation of the novel coronavirus is deteriorating day by day. There are more than 373 million cases recorded across the globe to date. The first incident of the disease was reported in China's Wuhan province in the month of December 2019. The virus is called COVID-19 which is an abbreviation for Corona Virus Disease 2019 or 2019-nCoV. The covid virus belongs to the same virus family as SARS, which stands for the severe acute respiratory syndrome. To date, approx. 5.5 million people have died due to the virus. The worst affected countries are the USA, India, Brazil, France, and Turkey. Most countries face the Second Wave of the coronavirus, which is more dangerous than the previous wave. India is currently passing through a rough phase, registering more than 50,000 positive cases daily for the past few weeks. Around 1000 people are dying every day as per the official data issued by government bodies. With a population of 1.4 billion people, it is challenging to break the chains of the spread of the disease. The country is facing serious medical shortages which include Oxygen support, medicines, ICUs, and Ventilators. A sudden surge of the cases created a panic situation across the country. It is nearly impossible to calculate the actual cases and the number of deaths. The conventional Rapid Antigen tests and RT-PCR tests (i.e., real-time polymerase chain reaction) are not completely efficient and quick. The Rapid Antigen tests have just 50 -60% accuracy, and the RT-PCR test takes 24 to 48 hours to declare whether a person is Covid positive or negative. Time plays an important role during covid. The early the infection can be detected, the early that person can start his/her medications, consult a doctor, and isolate himself/herself to prevent further spreading of the virus. So, in this study, X-Ray scans are used to ascertain whether a person is covid positive or not in few seconds. A Machine Learning model is also included in the study to forecast the number of instances in the next few days. © 2022 IEEE.

An IoMT-Based Federated and Deep Transfer Learning Approach to the Detection of Diverse Chest Diseases Using Chest X-Rays

Since chest illnesses are so frequent these days, it is critical to identify and diagnose them effectively. As such, this study proposes a model designed to accurately predict chest disorders by analyzing multiple chest x-ray pictures obtained from a dataset, consisting of 112,120 chest X-ray images, obtained the National Institute of Health (NIH) X-ray. The study used photos from 30,805 individuals with a total of 14 different types of chest disorder, including atelectasis, consolidation, infiltration, and pneumothorax, as well as a class called "No findings" for cases in which the ailment was undiagnosed. Six distinct transfer-learning approaches, namely, VGG-16, MobileNet V2, ResNet-50, DenseNet-161, Inception V3, and VGG-19, were used in the deep learning and federated learning environment to predict the accuracy rate of detecting chest disorders. The VGG-16 model showed the best accuracy at 0.81, with a recall rate of 0.90. As a result, the Fl score of VGG-16 is 0.85, which was higher than the Fl scores computed by other transfer learning approaches. VGG-19 obtained a maximum rate of accuracy of 97.71% via federated transfer learning. According to the classification report, the VGG-16 model is the best transfer-learning model for correctly detecting chest illness.

Deep Learning Based Covid - 19 Detection System Using Chest X-Ray Images

Coronavirus, a lung infection disease 2019 (COVID-19), is a sickness that is happening because of a virus, which is now also known as SARS-CoV-2 (Acute Respiratory Syndrome Coronavirus-2 COVID) was initially found in the city of China, named Wuhan. WHO was reported about COVID-19 disease on December 31st, year 2019.On January 30th, 2020, the WHO declared this outbreak a health emergency on a global level. On March 11th, 2020, it was announced as a worldwide pandemic by the World Health Organization. Coronavirus can trigger a tract infection. It affects a human being's respiratory tract-both lower or upper or both. In this research, we have proposed a rapid detection system for noticing COVID 19 disease in its early stages by using images of radiography(chest). Our model differentiates between two types of images, standard (non-COVID) and COVID infected. Since the images used for the training part for COVID infection are limited, we have used the Data Augmentation technique. Data Augmentation is a phenomenon that expands the dimensions of a dataset by producing altered versions of these images. This approach has proved to increase the efficiency of the model. © 2021 IEEE.