Multi-Organ Disease Detection With Enhanced Deep Learning Algorithms

Abstract

The project leverages deep learning techniques to develop a robust and efficient system capable of detecting diseases across multiple human organs. The increasing availability of medical imaging data and advancements in deep learning have made it possible to create models that can analyze complex patterns in medical data and assist healthcare professionals in early and accurate diagnosis. This project focuses on utilizing convolutional neural networks (CNNs) to analyze medical images such as X-rays, MRIs, or CT scans and classify diseases affecting different organs, including the heart, lungs, ovaries, and kidneys. By training on a diverse dataset, the model aims to identify early signs of disease, improving patient outcomes through timely intervention. The system's performance will be evaluated using key metrics such as accuracy, precision, recall, and F1-score, ensuring that it meets clinical standards. This project has the potential to streamline diagnostic processes, reduce human error, and enhance the decision-making process in medical practice.