A study of classification and feature extraction techniques for brain tumor detection

Medical imaging aids in the analysis of interior parts of the human body such as the functioning of the organs or tissues for early treatment of diseases. Many different types of medical imaging technologies exist, for example, X-ray radiography, magnetic resonance imaging, endoscopy, positron emission tomography, CT scan (computed tomography), and many more. A tumor is an abnormal tissue in the brain which causes damage to the functioning of the cell. Therefore, brain tumor detection is an incredibly tricky task. Manual detection of a tumor is quite risky as it involves the insertion of a needle in the brain. Thus, there is a need for automated brain tumor detection systems. The well-timed detection of the tumor can add to accurate treatment and can increase the survival rate of patients. From machine learning techniques, namely K-nearest neighbor, support vector machine, and more to soft computing techniques, namely artificial neural network, self-organizing map, and others hold a significant stand in detection and categorization of brain tumor. Various methods including deep learning-based classifiers such as convolutional neural network, recurrent neural network, deep belief network (DBN), and others are used to make it easier to detect the tumor. Hybrid classifiers were also used for classification systems such as combining the machine learning approach with soft computing. This study is to summarize and compare the work of various authors on automatic brain tumor detection using medical imaging. Based on the accuracy, specificity, and sensitivity parameters, the results of different techniques are analyzed and compared graphically.