A Short Review on the Role of Various Deep Learning Techniques for Segmenting and Classifying Brain Tumours from MRI Images

The past few years have observed substantial growth in death rates associated with brain tumors and it is second foremost source of cancer-related demises. However, it is possible to increase the chance of survival if tumors are identified during initial stage by employing various deep learning techniques. These techniques are helpful to the doctors during the diagnosis process. The MRI which refers to magnetic resonance imaging is a non-invasive procedure and low ionization radiation diagnostic tool to evaluate an abnormity that evolves in the form of shape, location or position, size and texture of tumour. This paper focuses on the systematic literature survey of numerous Deep-Learning methods with suitable approaches for tumour segmentation and classification (normal or abnormal) from MRI images. Furthermore, this paper also provides the new aspects of research and clinical solution for brain tumor patients. It incorporates Deep-Learning applications for accurate tumor detection and quantitative investigation of different tumor segmentation techniques.

@article{D2022,
  title     = {A Short Review on the Role of Various Deep Learning Techniques for Segmenting and Classifying Brain Tumours from MRI Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {9},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Kumari Kavitha. D and E. Kiran Kumar},
  doi       = {10.14569/IJACSA.2022.0130995},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130995}
}