Study and Analysis of Various Automatic Brain Tumour Segmentation andClassification: A Challenging Overview

Main Article Content

K.V. Shiny, et. al.


Early prediction of Brain Tumour (BT) plays a vital function for providing treatment plan and patient care at initial stage. The manual identification and classification of brain tumour is difficult to realize and time consuming due to the same structures, and also the scarcity of available radiology expertise. Generally, the BT classification is performed in two ways, such as:realize whether the image as either normal or abnormal (ii) Classification of abnormal region based on the various kinds of tumours. Thus, the manual classification of brain tumour from MR images are time consuming, nonreproducible and impractical due to the vast quantity of MRI dataset. To overcome these issues, automatic classification is a proper solution to classify the tumour from Magnetic Resonance (MR) images with less intervention of radiologists. The key challenge in classification of MR image is the semantic gap among low level visual data gathered through MRI machine and high level information perceived through human evaluator. The conventional machine learning-based classification techniques focused only in either low level or high level or handcrafted features for diminishing this gap and achieving better feature extraction and classification methods. Thus, this survey analyzes several techniques intended to BT segmentation and classification through images from various sources. This study utilized25 research papers concentrated on various techniques, and the review of various researches technique-wiseis also to be provided.Finally, the analysis discussed in this survey based on the publication year, research technique, implementation tools, performance measures and achievement of the research methodologies towards BT segmentation and classification using different datasets. At the end, the research gaps, issues of the techniques and then, the motivation for developing an effective method for brain tumour segmentation and classification is also to be revealed.

Article Details