Machine Learning Technique for Brain Tumor Detection: A Comprehensive Review

Detecting diseases in the brain at early stages or proper diagnosis and treatment of tumor in the brain are perhaps the biggest challenges facing medical sciences. New advancement in Machine Learning has developed novel methods for augmenting accuracy and effectiveness of detecting brain tumors based on image obtained through MRI scans. This review goes in-depth analysis of how detection and classification is currently improving, highlighting such techniques involving SVM, CNNs, ResNet, and Xception. Key spatial and textural feature extraction techniques, like PCA, HOG, and GLCM, have also been considered in order to improve the classification performance. Ensemble approaches, such as Random Forest, have been taken into consideration for handling the complexity of the dataset. The study evaluates the benefits, limitations, and clinical applicability of these methods in view of the challenges posed by diverse datasets, high computational demands, and generalization issues. Further research and development are required to make the machine-learning diagnostic capabilities of brain-related conditions more reliable and practical.