Background:
This paper endeavors to identify an expedient approach for the detection
of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine
learning approach for the identification of brain tumor and ii) review of a suitable approach for
brain tumor detection.
Discussion:
This review focuses on different imaging techniques such as X-rays, PET, CT- Scan,
and MRI. This survey identifies a different approach with better accuracy for tumor detection.
This further includes the image processing method. In most applications, machine learning shows
better performance than manual segmentation of the brain tumors from MRI images as it is a difficult
and time-consuming task. For fast and better computational results, radiology used a different
approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this
paper also provides a critical evaluation of the surveyed literature which reveals new facets of research.
Conclusion:
The problem faced by the researchers during brain tumor detection techniques and
machine learning applications for clinical settings have also been discussed.
Design of Microwave-based Brain Tumor Detection Framework with the Development of Sparse and Low-Rank Compressive Sensing Image Reconstruction