Optimizing Brain Tumor Detection using Machine Learning

Abstract

The detection of brain tumors holds paramount importance within medical imaging, where early identification is crucial for effective treatment and improved patient outcomes. This review offers an extensive overview of the diverse techniques utilized for brain tumor detection, focusing on advancements up to September 2021.

Initiating with a discussion on conventional imaging methods such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), pivotal in brain tumor diagnosis for decades, the review underscores their role in furnishing comprehensive structural insights, serving as the cornerstone for contemporary diagnostic methodologies.

Subsequently, the review delves into the pioneering utilization of machine learning, notably deep learning models, for brain tumor detection. Convolutional Neural Networks (CNNs) and other deep learning architectures have revolutionized the domain by streamlining the detection process. Excelling in feature extraction and classification, these models facilitate precise tumor identification from medical images.

Moreover, the review investigates the integration of multi-modal data, amalgamating information from various imaging techniques to bolster diagnostic precision.