Record Details

<p>Brain Tumor Classification using SLIC Segmentation with Superpixel Fusion, GoogleNet, and Linear Neighborhood Semantic Segmentation</p>

Online Publishing @ NISCAIR

View Archive Info
 
 
Field Value
 
Authentication Code dc
 
Title Statement <p>Brain Tumor Classification using SLIC Segmentation with Superpixel Fusion, GoogleNet, and Linear Neighborhood Semantic Segmentation</p>
 
Added Entry - Uncontrolled Name Naik, Snehalatha ; Poojya Doddappa College of Engineering, Aiwan-E-Shahi Area, Shambhognlli, Kalaburagi 585 102, Karnataka, India
Patil, Siddarama ; Poojya Doddappa College of Engineering, Aiwan-E-Shahi Area, Shambhognlli, Kalaburagi 585 102, Karnataka, India
 
Uncontrolled Index Term Border pixels, CNN, MRI, Total variation denoising
 
Summary, etc. <p>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%.</p>
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2023-02-09 21:08:13
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/70214
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 82, ##issue.no## 02 (2023)
 
Language Note en