• Kajabe Yogita Babaji
• Dr.Brijendra Gupta

The timely and precise detection of lung diseases, such as pneumonia, cancer, and COVID-19, a common and possibly fatal respiratory infection, is extremely difficult. The goal of this project is to create a dependable and effective pneumonia detection system by utilizing developments in machine learning (ML) techniques. A convolution neural network (CNN) model is trained to distinguish between lung images that are healthy and those that have pneumonia by analyzing chest CT images. A wide variety of chest CT pictures gathered from several sources make up the dataset used for training and validation. The suggested methodology shows encouraging outcomes, detecting pneumonia with good sensitivity and accuracy. Long-term damage to the lungs and other organs is another potential COVID-19 risk. The COVID-19 virus is primarily spread by droplets released when an infected person coughs, sneezes, or exhales. These drops swiftly land on floors or other objects because they are too heavy to float in midair. As everyone knows, in early 2020, the corona virus illness 2019 (COVID-19) began to spread around the world. resulting in an existential health problem for the entire world. Therefore, there is a lot of opportunity to enhance the conventional healthcare approach to COVID-19 by automating the detection of lung infections from computed tomography (CT) images. However, there are a number of difficulties in separating infected areas from CT slices, such as the wide range of infection traits and the weak contrast between normal tissues and infections. Furthermore, it is impractical to gather a lot of data in a short amount of time, which prevents a deep model from being trained. Our suggested method will examine the lung's CT scan to identify the infected area and the proportion of the lung that is impacted. The system will determine the severity of the inflection and assist patients in taking necessary action.

Machine learning, Convolution neural network, Lung Infection, pneumonia, COVID-19, CT Images

"Deep learning for Lung Infection Segmentation and prediction of COVID-19 from CT Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.a383-a386, November-2024, Available :http://www.jetir.org/papers/JETIR2411042.pdf

"Deep learning for Lung Infection Segmentation and prediction of COVID-19 from CT Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppa383-a386, November-2024, Available at : http://www.jetir.org/papers/JETIR2411042.pdf