IDSS-based Two stage classification of brain tumor using SVM.

Autor: Polepaka, Sanjeeva, Rao, Ch. Srinivasa, Chandra Mohan, M.
Zdroj: Health & Technology; Jan2020, Vol. 10 Issue 1, p249-258, 10p
Abstrakt: The computer and image processing has a significant role in detecting tumor area. The decision support systems for human brain MR images are essentially encouraged with the requirement of attaining maximal achievable efficiency and the motivation of the approach which is to enhance the performance of Computer-Aided Diagnosis (CAD) system to detect a tumor in the human brain. Even though numerous support systems have been introduced in the past, this is still an open problem seeking for an accurate and robust decision support system. The Interactive Diagnosis Support System (IDSS) approach has addressed the limitations of nonillumination and low contrast of a brain tumor MR image that influences the procedure of accurate image classification. Thus, the IDSS is implemented in three phases namely image preprocessing for enhancing non-illuminated features, feature extraction and image classification which is accomplished using two-stage interactive SVM Classification. The local binary patterns are detected in the feature extraction for accurate classification of usual and unusual brain MR Images. The experimental outcomes for this approach are carried out using MATLAB R2016a and evaluated using the brain images downloaded from the Internet. The performance metrics such as structured similarity index, sensitivity, specificity and accuracy were used to assess the IDSS-based tumor classification system. When compared with the traditional classifiers such as ANFIS, Backpropagation and K-NN, the IDSS approach has significant brain tumor classification accuracy. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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