Abstract
This paper presents a novel APSO (Accelerated Particle Swarm Optimization) Predicated LLRBFNN (Local Linear Radial Basis Function Neural Network) model for automatic encephalon tumor detection and classification. The enhanced fuzzy c means algorithm (EnFCM) has been proposed for image segmentation and the GLCM (Gray Level Co-occurrence Matrix) technique for feature extraction from MR (Magnetic Resonance) images. This research work aims to utilize the hybrid models and algorithms for relegation and segmentation of encephalon tumors from the MR images. The extracted features have been alimented as input to the proposed APSO predicated LLRBFNN model for relegation of benign and malignant tumors. In this research work the proposed LLRBFNN model weights are optimized by utilizing APSO training which will provide unique solution to mitigation the hectic task of radiologist from manual detection of encephalon tumors from MR Images. Additionally the centers of the LLRBFNN model are culled by the Enhanced Fuzzy C Means algorithm and updated by the APSO algorithm. The results of proposed PSO predicated LLRBFNN model has been compared with PSO-LLRBFNN model, APSO-RBFNN and PSO-RBFNN model and the comparison results are presented. The experimental results obtained from the proposed model shows better relegation results as compared to the subsisting models proposed anteriorly.
Highlights
The manual analysis of tumor predicated on visual interpretation by radiologist may lead to erroneous diagnosis when the number of images increases
The research work follows the steps such as (i) MR images has been first segmented by the Enhanced Fuzzy C Means algorithm and the features has been extracted from the images utilizing GLCM (Gray Level Co-occurrence Matrix) feature extraction technique
At the first step the images are segmented by the enhanced fuzzy c means algorithm (EnFCM) algorithm and the features are extracted by GLCM [25] feature extraction technique
Summary
The manual analysis of tumor predicated on visual interpretation by radiologist may lead to erroneous diagnosis when the number of images increases. EnFCM is predicated on a simple fact that, the number of gray levels q is generally much more minuscule than the size N of the image. By utilizing this fact, the time involution of EnFCM can be drastically reduced. The parameter α is utilized to control the trade-off between the pristine image and its corresponding mean- or median-filtered image[5].Using Enhanced FCM, the segmenting time will be reduced compared with the standard FCM. The research work follows the steps such as (i) MR images has been first segmented by the Enhanced Fuzzy C Means algorithm and the features has been extracted from the images utilizing GLCM (Gray Level Co-occurrence Matrix) feature extraction technique. The relegation results of malignant and benign tumor from the proposed
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