Thesis Abstract

Abstract
The advent of artificial intelligence (AI) has revolutionized various industries, including healthcare. AI technologies, particularly deep learning algorithms, have shown great promise in medical image analysis, diagnosis, and treatment planning. This research explores the applications of deep learning in medical imaging, focusing on its impact on radiology and pathology. Deep learning models have been successfully used to assist radiologists in interpreting medical images, improving diagnostic accuracy and efficiency. In pathology, deep learning algorithms have been utilized for image analysis, aiding in the detection and classification of various diseases. The integration of AI in medical imaging has the potential to enhance patient outcomes by enabling early detection of diseases, personalized treatment planning, and reducing human errors. However, challenges such as data privacy concerns, lack of interpretability, and regulatory issues need to be addressed to fully realize the benefits of deep learning in medical imaging. Overall, the utilization of deep learning in medical imaging has the potential to revolutionize healthcare by providing more accurate and efficient diagnostics, ultimately improving patient care.

Thesis Overview