Brain Tumor Detection Techniques: A Review

According to the International Agency for Research on Cancer (IARC), brain tumors have a high mortality rate of 76%. Early detection is critical to providing timely treatment and preventing fatal outcomes. With advancements in technology, it has become possible to automatically identify tumors from images, such as Magnetic Resonance Imaging (MRI) and computed tomography (CT) scans, using computer-aided design. Machine learning and deep learning techniques, particularly Convolutional Neural Networks (CNNs), have become increasingly important in the medical field due to their ability to process and classify large, complex image datasets. This review article aims to provide a comprehensive overview of the techniques, including preprocessing, machine learning, and deep learning, that have been used over the past 15 years for brain tumor detection. Additionally, it offers a detailed comparative analysis of these approaches. Keywords Brain Tumor, Machine Learning, Deep Learning, MRI