Integrated Model for Covid 19 Disease Diagnosis using Deep Learning Approach

The severe Corona Virus Disease-2019 (COVID-19), which is caused by the severe acute respiratory syndrome-Corona Virus-2 (SARS-CoV-2), has killed millions of people worldwide. Imaging methods like Chest X-rays (CXR) and Computed Tomography (CT) are frequently utilised to diagnose COVID-19 quickly and reliably. However, manual identification of infections through radiographic imaging is challenging, time-consuming, and prone to human error. Deep learning, particularly Convolutional Neural Networks (CNN), is the preferred approach for identifying and extracting features from such medical images. This study employs CNN to differentiate between healthy lungs, lungs affected by COVID-19, and viral pneumonia-affected lungs. The ultimate goal of categorizing medical data is to develop a model or tool that can distinguish between various diseases and healthy individuals or predict disease status. To address these challenges, a new network named ResNet50 is developed for lung CT segmentation with the aim of creating an effective neural network that uses fewer training images.