Combining Wavelet Texture Features and Deep Neural Network for Tumor Detection and Segmentation Over MRI

Journal of Intelligent Systems

Aishwarya Palaniappan¹  Preethi Srinivasalu² 

[1] Computer Science Department, Atria Institute of Technology, Bangalore, India;Visvesvaraya Technological University (VTU), Jnana Sangama, Machhe, Belagavi, Karnataka 590018, India;

关键词: brain tumor;    wavelet texture feature;    deep neural network;    gray-level co-occurrence matrix;    oppositional flower pollination algorithm;    possibilistic fuzzy c-means clustering;   

DOI  :  10.1515/jisys-2017-0090

来源: DOAJ

【 摘 要 】

A brain tumor is one of the main reasons for death among other kinds of cancer because the brain is a very sensitive, complex, and central portion of the body. Proper and timely diagnosis can prolong the life of a person to some extent. Consequently, in this paper, we have proposed a brain tumor classification scheme on the basis of combining wavelet texture features and deep neural networks (DNNs). Normally, the system comprises four modules: (i) feature extraction, (ii) feature selection, (iii) tumor classification, and (iv) segmentation. Primarily, we eliminate the noise from the image. Then, the feature matrix is produced by combining wavelet texture features [gray-level co-occurrence matrix (GLCM)+wavelet GLCM]. Following that, we select the relevant features with the help of the oppositional flower pollination algorithm (OFPA) because a high number of features are major obstacles for classification. Then, we categorize the brain image based on the selected features using the DNN. After the classification procedure, the projected scheme extracts the tumor region from the tumor images with the help of the possibilistic fuzzy c-means clustering (PFCM) algorithm. The experimentation results show that the proposed system attains the better result associated with the available methods.

【 授权许可】

Unknown