Turkish Journal of Computer and Mathematics Education
Journal license

Journal

Turkish Journal of Computer and Mathematics Education


Volume
& Issue

Volume 10, Issue 1


Published
on


Pages

602-611


DOI

Article

An Implementation of Deep Wavelet Auto Encoder-Based Deep Neural Network Brain MRI Image Classification for Cancer Detection

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Authors

Navin Garg Affiliation:
Graphic Era Hill University, Dehradun Uttarakhand India 248002


Abstract

Both computational intelligence and pattern recognition depend on Brain lesion segmentation and classification. In this procedure, an effective algorithm was used to segment the lesion, and its characteristics, including LBP, were paired with the GLCM to extract the data from the picture. A morphologically based fuzzy C-means clustering technique (M-FCM) is suggested for clustering in segmentation. The severity of the information from the brain is then classified using CNN utilising the procedure used in the medical profession to detect brain lesions. The major goals of this procedure are to locate the malignant area on an MRI of the brain and to categorise the severity of that brain in order to increase process effectiveness.


Keywords


Citation

Garg, N. (2019). An implementation of deep wavelet auto encoder-based deep neural network brain MRI image classification for cancer detection. Turkish Journal of Computer and Mathematics Education, 10(1), 602–611. https://doi.org/10.17762/turcomat.v10i1.13555

Published by: Engineering Journals

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