COVID 19 Segmentation of CT Lung Images using custom U-Net Architecture: COVID 19 CT Image Segmentation To study the segmentation process and work on the CT Image segmentation insights from segment radiology on axial lung slices using the U-Net method. The objective of segmenting consolidations in COVID-19 lung CT scans is to provide a more detailed and accurate view of the degree and severity of COVID-19 lung involvement in patients. The primary goal is to detect disease-affected regions of the lung and assess the degree of involvement. This allows radiologists and doctors to better assess the severity of each patient's condition and select the best course of treatment. By this analysis we are going to predict whether the person is having the Corona Virus or not and how much the lungs were affected can be analyzed in this process. Record the observations obtained on implementing the CT Image Segmentation. Dataset: Segmentation of lung changes from CT images (https://www.kaggle.com/c/covidsegmentation/data) a. Segmentation of consolidations only with the use of custom U-Net architecture (max 70%) Purpose: The purpose of COVID-19 segmentation is to identify and segregate people who are infected from those who are not. This is done to assist prevent the virus's spread and to give appropriate care to people who have been afflicted. Aside from testing and isolation, segmentation may also entail identifying and monitoring close contacts of individuals who have tested positive for COVID-19, as well as implementing measures such as mask use, hand cleanliness, and so on. The overall purpose of COVID-19 segmentation is to prevent the virus's propagation and protect public health segmentation of consolidations in COVID-19 lung CT scans is a useful tool for detecting and managing COVID-19 patients, and it can assist improve illness outcomes. For complete information regarding the ppt description please check out the link: https://covid-19segmentationoflungctimages.blogspot.com/2023/03/segmentation-of-lung-changes-from-ct.html