AI-Driven Deep Learning Models for Enhanced Medical Image Diagnosis

Abstract

Computer-aided diagnosis (CAD) using Artificial Intelligence (AI) has emerged as a powerful tool to improve accuracy and efficiency in medical image interpretation. This thesis investigates AI techniques for diagnosing various pathologies through medical imaging, covering a broad spectrum of diseases, from tuberculosis to post-COVID conditions. The primary objective of this study was to develop and evaluate AI algorithms for the automatic detection and classification of diseases using medical images. MRI datasets, chest X-rays, and other imaging modalities were employed to train and test the proposed AI models. Various deep learning techniques, such as convolutional neural networks, were implemented to perform classification and detection tasks on medical images. The results demonstrate a high degree of accuracy and sensitivity in disease detection, often surpassing the performance of traditional diagnostic methods. A significant improvement in early disease detection was observed, which could lead to more timely treatment and better patient outcomes. This research has significant clinical implications and highlights AI's potential to contribute to medical diagnosis. Current limitations are discussed, and future research directions are proposed in this exciting and promising field of study.