Deep learning based semantic segmentation approach for automatic detection of brain tumor

International Journal of Computers Communications & Control

article

S. Markkandeyan¹  Shivani Gupta²  G. Venkat Narayanan³  M Jithender Reddy⁴  Mahmoud Ahmad Al-Khasawneh⁵  Mohammad Ishrat⁶  Ajmeera Kiran⁷ 

[1] School of Computing, SASTRA Deemed University;School of Computer Science and Engineering, Vellore Institute of Technology Chennai;St. Joseph’s College of Engineering Kanchipuram;Vasavi College of Engineering;Department of Information Technology, School of Computing, Skyline University College, University City Sharjah Sharjah;Computer Science and Engineering, Koneru Lakshmaiah Education Foundation Vaddeswaram;Deptartment of Computer Science and Engineering MLR Institute of Technology

关键词: Brain Tumor;    deep learning;    artificial intelligence;    semantic segmentation;    MRI images;    GoogLeNet CNN classification;    convolutional neural network;    bias-corrected filtering;   

DOI  :  10.15837/ijccc.2023.4.5186

学科分类：自动化工程

来源: Universitatea Agora

【 摘 要 】

Initially, fromBRATS 2013 dataset the input image is acquired and is preprocessed, segmented using Convolutional neural network (CNN) based semantic segmentation, and were classified using Improved multipath GoogLeNe tCNNclassifier approach.The stage of preprocessing is carried using Bias corrected filtering. Ascheme of deep learning-based semantic segmentation of brain tumors for MRI images is proposed to classify the brain tumor effectively. In this approach, semantic segmentation is employed for segmentation purposes. Improved multipath GoogLeNetCNNis applied for classifying Brain tumor for classifying brain MR images and for grading the brain tumors into three classes (Meningioma, Pituitary Tumor, and Glioma). There are a total of 3064 T1-weighted contrast- enhanced pictures representing 233 patients in the input dataset.Accuracy, sensitivity, specificity, & precision estimates for the proposed method are calculated in MATLAB. The obtained results expose that the projected method achieves overall classifier performance rate of 99.7accuracy, sensitivity 100%, specificity 99.717%, and precision of 99.06%. Results show that the suggested system outperforms state-of-the-art methods.

【 授权许可】

CC BY-NC