Leveraging Artificial Intelligence for Brain Tumor Classification

Brain cancer, characterized by the uncontrolled growth of abnormal cells in the brain, is a severe neurological disorder that can be either primary or metastatic. Early detection and accurate classification of brain tumors are crucial for effective management and improved patient outcomes. Brain tumors are classified based on various factors such as their nature, cell origin, grade, and progression stage. Traditional methods of detection, segmentation, and classification are time-consuming, require extensive expertise, and are prone to errors. Artificial Intelligence (AI), including its subtypes Machine Learning (ML) and Deep Learning (DL), holds promise for improving accuracy and expediting detection. AI-based technologies can be categorized into binary classification (e.g., determining whether a tumor is malignant or benign) and multimodal classification (e.g., categorizing tumors into various types). Most AI applications in brain tumor classification focus on radiological images, particularly Magnetic Resonance Imaging (MRI).

AI-based technologies must achieve high accuracy to be effectively integrated into real-life clinical practice. This chapter summarizes the current advances in AI techniques for brain tumor classification, highlighting their potential and ongoing challenges.