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Brain Tumor Classification using SLIC Segmentation with Superpixel Fusion, GoogleNet, and Linear Neighborhood Semantic Segmentation

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Title Brain Tumor Classification using SLIC Segmentation with Superpixel Fusion, GoogleNet, and Linear Neighborhood Semantic Segmentation
 
Creator Naik, Snehalatha
Patil, Siddarama
 
Subject Border pixels
CNN
MRI
Total variation denoising
 
Description 255-262
Brain tumor is an abnormal tissue mass resultant of uncontrolled growth of cells. Brain tumors often reduce life
expectancy and cause death in the later stages. Automatic detection of brain tumors is a challenging and important task in
computer-aided disease diagnosis systems. This paper presents a deep learning-based approach to the classification of brain
tumors. The noise in the brain MRI image is removed using Edge Directional Total Variation Denoising. The brain MRI
image is segmented using SLIC segmentation with superpixel fusion. The segments are given to a trained GoogleNet model,
which identifies the tumor parts in the image. Once the tumor is identified, a Convolution Neural Network (CNN) based
modified semantic segmentation model is used to classify the pixels along the edges of the tumor segments. The modified
sematic segmentation uses a linear neighborhood of the pixel for better classification. The final tumor identified is accurate
as pixels at the border are classified precisely. The experimental results show that the proposed method has produced an
accuracy of 97.3% with GoogleNet classification model, and the linear neighborhood semantic segmentation has delivered
an accuracy of 98%.
 
Date 2023-02-08T05:05:58Z
2023-02-08T05:05:58Z
2023-02
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61358
https://doi.org/10.56042/jsir.v82i2.70214
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(02) [February 2023]