ABSTRACT
A brain tumor is one of the most perilous diseases in human beings. The manual segmentation of brain tumors is costly and takes a lot of time; due to this reason, automated approaches are highly valued. Automated brain tumor detection by radiologists is a difficult task for the surveillance of patients. Early detection of brain tumors has a significant role in enhancing the effectiveness of treatment and further increasing the survival of patients. Brain tumor detection is a challenging task for radiologists and physicians. It is difficult to examine the brain tumor through image processing generated in the medical. Thus, for early brain tumor detection, there is a critical requirement for computer-aided approaches with higher accuracy. Multi-model images nowadays are increasing the interest in the classification of brain tumors. The detection of brain tumors through magnetic resonance images (MRIs) consists of segmentation and classification methods. Many experiments have been done over the last few years on machine learning (ML) for brain tumor segmentation and classification. Recently, interest has increased in the use of deep learning (DL) approaches to help the detection of brain tumors. MRI is commonly used to detect the brain tissues according to size, shape or location, and that aids to detect the tumor. The main goal of this chapter is to help researchers extract the essential features of brain tumors detection and identify the different automated segmentation and classification techniques that are successful in using multimodal MRIs to detect brain tumors. Additionally, this chapter provides a comprehensive review and comparative study of various automated brain tumor classification methods/techniques built on ML and DL methods of brain tumor detection from MRI. The different strategies of ML and DL, including K-means clustering, fuzzy C-means, K-nearest neighbor, support vector machine, decision tree, G-convolutional neural network, artificial neural network (ANN), conditional random field-recurrent neural network (CRF-RNN), deep neural network (DNN), Naive Bayes, etc. are used in this work. Some famous methods are generally used to create valuable data from medical image processing techniques. The key achievements represented the algorithms performance measurement metrics that are identified in this chapter. The proposed methods by researchers are considered for the Medical Image Computing and Computer-Assisted Intervention (MICCAI) challenges on benchmark brain tumor segmentation (BRATS) 2012–2019, and many other different datasets for segmentation and classification of brain tumors are used in this chapter. The researchers have used several techniques for the segmentation, classification and detection of a brain tumor on various datasets for achieving the best performance. Automatic brain tumor detection through MRIs is essential, as high accuracy is required when working with human life. This study helps the researchers to choose the best method/technique with the use of different datasets.
