Future Diagnosis: Detection of Pneumonia and Pandemic Pathogen using CNN

Artificial Intelligence (AI) has seen widespread application across various sectors, with recent decades witnessing a surge in available data. The primary objective of this research study is to facilitate better, faster, and more reliable decision-making processes. In the pharmaceutical industry, there has been an increasing reliance on Deep Learning (DL) and AI, particularly in image processing, to enhance unity and improve the accuracy of diagnostics, especially in fields reliant on biomedical imaging. This study focuses on leveraging DL techniques to identify COVID-19 and pneumonia through the visualization of X-ray images. Currently, diagnosing pneumonia, especially in conjunction with COVID-19, requires multiple tests. The study utilizes data collected from Kaggle to evaluate the performance of a Convolutional Neural Network (CNN) trained on X-ray images. The aim is to validate and predict COVID-19 infections and pneumonia based on chest X-ray detection. By training and evaluating CNN models on X-ray images, the study seeks to provide a reliable method for detecting COVID-19 and pneumonia, potentially streamlining diagnostic processes and improving patient outcomes.