Applications of Artificial Intelligence in the Classification of Magnetic Resonance Images: Advances and Perspectives

Abstract

This chapter examines the advances and perspectives of the applications of artificial intelligence (AI) in the classification of magnetic resonance (MR) images. It focuses on the development of AI-based automatic classification models that have achieved competitive results compared to the state-of-the-art. Accurate and efficient classification of MR images is essential for medical diagnosis but can be challenging due to the complexity and variability of the data. AI offers tools and techniques that can effectively address these challenges. The chapter first addresses the fundamentals of artificial intelligence applied to the classification of medical images, including machine learning techniques and convolutional neural networks. Here, recent advances in the use of AI to classify MRI images in various clinical applications, such as brain tumor detection, are explored. Additionally, advantages and challenges associated with implementing AI models in clinical settings are discussed, such as the interpretability of results and integration with existing radiology systems. Prospects for AI in MR image classification are also highlighted, including the combination of multiple imaging modalities and the use of more advanced AI approaches such as reinforcement learning and model generation.