| Abstrakt: |
Previously, the brain tumour segmentation is carried out as the manual process for detecting the brain tumour from the huge quantity of Medical Resonance Images (MRI) that is obtained from the clinical practices. But, these types of manual segmentation require more time and become a tedious process. While analysing the medical images, the challenges occur in detecting the brain tumours through MRI. This recognition process becomes difficult because of certain complexities and the presence of numerous varieties of tumour tissues. This analysis is aimed to summarise the semi-automatic methods for segmenting and classifying the brain tumour MRI and other modalities. The main purpose of this paper is to provide a literature review of brain tumour segmentation and classification using different imaging modalities. At first, the different tumour segmentation and feature extraction techniques are depicted. Further, the recent trend of deep and machine learning methods in this field is reviewed and categorised. The datasets used in different contributions, the simulated platforms, and the performance measures analysed are clearly evaluated and sorted out. Finally, the unsolved challenges under this field are observed, and future advancements to improve MRI-based brain tumour detection methods in the regular clinical routine are considered. [ABSTRACT FROM AUTHOR] |
