• Venkata Sai Lokesh Chunduri
• B. Pranalini
• Raj Kumar Barik
• Rongali Yuva Kiran
• Vuriti Eswar

This project presents the development of a computer-aided diagnosis system for pneumonia detection using chest X-ray images. Utilizing Convolutional Neural Network (CNN) architecture, the system is designed to analyze and classify chest X-ray images, distinguishing between those indicative of pneumonia and normal lung conditions. The dataset used consists of chest X-ray images obtained from publicly available repositories. Moreover, our project includes the design of a user-friendly web interface using Streamlit, a Python library for building interactive web applications. This web page allows users to upload chest X-ray images as input and receive the corresponding diagnosis output. By incorporating this system with a user-friendly web interface, we aim to provide a seamless and accessible tool for early pneumonia detection, facilitating timely medical intervention and improving patient outcomes. Additionally, the system's performance will be evaluated using metrics such as accuracy, sensitivity, and specificity to ensure its reliability and effectiveness in clinical settings. The system is designed to be time-saving and affordable, suitable for a wide range of users, including those with no technical knowledge, thanks to its ease of use. It is highly accurate and reliable, contributing to its effectiveness in diagnosing pneumonia and improving patient care.

"Pneumonia detection using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.f733-f741, July-2024, Available :http://www.jetir.org/papers/JETIR2407593.pdf

"Pneumonia detection using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppf733-f741, July-2024, Available at : http://www.jetir.org/papers/JETIR2407593.pdf