A Survey of Brain Tumor Segmentation and Classification Algorithms

Abstract

A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.

A novel 2-phase residual U-net algorithm combined with optimal mass transportation for 3D brain tumor detection and segmentation

“…To address this issue, in the early years, random forest algorithms and machine learning techniques were used to perform image classification 6 – 8 and segmentation 6 , 9 – 11 . In 2021, Biratu et al provided a significantly comprehensive survey 12 of three model techniques, including region growing 13 , shallow machine learning 14 , and deep learning 15 , for brain tumor segmentation and classification. Later, BraTS 2021 3 , 4 , 16 was expanded to include a large number of new brain samples in the database, providing 1251 labeled samples for training and 219 unlabeled samples for validation.…”

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“…A brain tumor, a tumor that develops within the skull, is an abnormal mass of tissue in which cells grow and multiply out of control. Although more than 150 types of brain tumors have been reported, they are macroscopically divided into primary and metastatic groups [ 12 ]. Tumors that arise directly from the brain tissue or surrounding the brain are classified as primary brain tumors.…”

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“…A report from United News of India (UNI) reveals that, per year 28,142 new brain tumor cases https://www.indjst.org/ were reported with a very high mortality rate of about 24,003 in the year 2018 (2) . A survey is made to derive the characteristics of brain tumor and segmentation or classification techniques for successful detection of brain tumor (3) .…”