Abstract
Various computer systems have attracted more researchers’ attention to arrive at a qualitative diagnosis in a few times. Different brain tumor classification approaches are proposed due to lesion complexity. This complexity makes the early tumor diagnosis using magnetic resonance images (MRI) a hard step. However, the accuracy of these techniques requires a significant amelioration to meet the needs of real-world diagnostic situations. We aim to classify three brain tumor types in this paper. A new technique is suggested which provides excellent results and surpasses the previous schemes. The proposed scheme makes use of the normalization, dense speeded up robust features, and histogram of gradient approaches to ameliorate MRI quality and generate a discriminative feature set. We exploit support vector machine in the classification step. The suggested system is benchmarked on an important dataset. The accuracy achieved based on this scheme is 90.27%. This method surpassed the most recent system according to experimental results. The results were earned through a strict statistical analysis (k-fold cross-validation), which proves the reliability and robustness of the suggested method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.