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

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 morpholo...

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Bibliographic Details
Published inTurkish journal of computer and mathematics education Vol. 10; no. 1; pp. 602 - 611
Main Author Garg, Navin
Format Journal Article
LanguageEnglish
Published 10.04.2019
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Summary: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.
ISSN:1309-4653
1309-4653
DOI:10.17762/turcomat.v10i1.13555