A review: Machine learning techniques of brain tumor classification and segmentation

Classifying brain tumors in magnetic resonance images (MRI) is a critical endeavor in medical image processing, given the challenging nature of automated tumor recognition. The variability and complexity in the location, size, shape, and texture of these lesions, coupled with the intensity similarities between brain lesions and normal tissues, pose significant hurdles. This study focuses on the importance of brain tumor detection and its challenges within the context of medical image processing. Presently, researchers have devised various interventions aimed at developing models for brain tumor classification to mitigate human involvement. However, there are limitations on time and cost for this task, as well as some other challenges that can identify tumor tissues. This study reviews many publications that classify brain tumors. Mostly employed supervised machine learning algorithms like support vector machine (SVM), random forest (RF), Gaussian Naive Bayes (GNB), k-Nearest Neighbors (K-NN), and k-means and some researchers employed convolutional neural network methods, transfer learning, deep learning, and ensemble learning. Every classification algorithm aims to provide an accurate and effective system, allowing for the fastest and most precise tumor detection possible. Usually, a pre-processing approach is employed to assess the system's accuracy; other techniques, such as the Gabor discrete wavelet transform (DWT), Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Principal Component Analysis (PCA), Scale-Invariant Feature Transform (SIFT) and the descriptor histogram of oriented gradients (HOG). In this study, we examine prior research on feature extraction techniques, discussing various classification methods and highlighting their respective advantages, providing statistical analysis on their performance.