Integrated Detection for Covid by using x- ray images

The project <b>"Integrated Detection for Covid"</b> develops a machine learning-based system to detect COVID-19 using chest X-rays. It uses convolutional neural networks (CNNs) to classify images as "COVID" or "Normal," offering a fast and accessible screening tool to support healthcare providers, especially in areas with limited RT-PCR testing.Key Features:<b>Automated Detection</b>: Quick and accurate classification of chest X-rays.<b>High Performance</b>: Achieves reliable accuracy in distinguishing COVID-19 cases.<b>User-Friendly Interface</b>: Enables easy use for healthcare professionals.Methodology:The system involves data collection, preprocessing, training a CNN model, evaluation using metrics like accuracy, and deploying a simple user interface.Objectives:<b>Primary Goal</b>: Develop a reliable machine learning model capable of high-accuracy classification of chest X-rays into "COVID" or "Normal."<b>Efficiency</b>: Provide a tool that facilitates rapid preliminary screening in environments with limited testing resources.<b>Support for Healthcare</b>: Assist professionals with automated insights, reducing reliance on manual interpretations.Results:Achieved high accuracy in distinguishing between "COVID" and "Normal" cases.Demonstrated effective performance in rapid screening and decision-making.