--> The outbreak of the novel Corona virus disease (COVID-19) in December 2019 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) on 11th of March 2020. --> (RT-PCR) is the standard method for detection of COVID-19 disease, but it has many challenges such as false positives, low sensitivity, expensive, and requires experts to conduct the test. --> Chest X-ray (CXR) scan images can be considered as an alternative as they are fast to obtain and easily accessible. --> There are number of approaches to classify CXR images and detect the COVID-19 infections, the majority of these approaches can only recognize two classes (e.g., COVID-19 vs. normal) --> The current work proposes the use of a deep learning approach based on trained Keras models. --> This model performs two-way classification ,three-way classification and four-way classification
In this Project we aim to dive into a Present Societal Pandemic issue which we are facing around us past 2 years due to outbreak of Novel Corona Virus. -> Getting tested for covid-19 virus is not an easy deal with costly RT-PCR test, and delayed results, and with its no. of variants with different mutations emerging everyday all the new methods found to detect the virus and its variant have either become :
- Ineffective as all tests may not find each of the variants.
- Each of them a set a finical restrictions for the technology used.
- Each test has its own detection time . -> Chest X-Ray already exists and overcomes most of the above drawbacks, but still fail to give long term effects or severity. -> So as a Solution We Aim to develop a model to give large no. of classifications and comparisons of Covid Patients and whether it leads to pneumonia disease , also these models could be trained to classify long term effects after years of infection how things could change w.r.t chest infections, and lead to other chronic disease.
Go through Gautham_PDUDL_report.pdf to understand more It has complete information regarding the project and its Implementation
