Preprint Concept Paper Version 1 Preserved in Portico This version is not peer-reviewed

Comparative Study of Convolution Neural Network with GLCM Approach and Superpixel based Spectral Clustering for Brain Tumor Detection

Version 1 : Received: 10 July 2022 / Approved: 20 July 2022 / Online: 20 July 2022 (05:28:57 CEST)

How to cite: Kaur, G.; Sharma, S. Comparative Study of Convolution Neural Network with GLCM Approach and Superpixel based Spectral Clustering for Brain Tumor Detection. Preprints 2022, 2022070294. https://doi.org/10.20944/preprints202207.0294.v1 Kaur, G.; Sharma, S. Comparative Study of Convolution Neural Network with GLCM Approach and Superpixel based Spectral Clustering for Brain Tumor Detection. Preprints 2022, 2022070294. https://doi.org/10.20944/preprints202207.0294.v1

Abstract

Extensive growth in the volume of irregular brain cells is known as brain tumor. Human brain is surrounded by stiff skull. There are various issues that occur due to the growth of any tumor inside this restricted space. The malignant and benign are two main categories of the brain tumor. The skull is pressurized to enlarge from inside in case of growth of any benign or malignant tumor. This tumor leads to harm in brain and it may be dangerous to life also. The brain tumor is divided into two kinds - primary or secondary. The brain tumor detection techniques have various phases. In this paper, comparative study of CNN with GLCM approach and superpixel based spectral clustering is done tumor. This work takes into account metrics like accuracy, sensitivity and specificity for drawing the comparison between both the techniques.

Keywords

CNN; brain tumor; GLCM; segmentation; superpixel; spectral clustering

Subject

Computer Science and Mathematics, Computer Science

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