ICTACT Journal on Image and Video Processing

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Performance Analysis of SVM and Deep Learning with CNN for Brain Tumor Detection and Classification


Affiliations
1 Department of Electrical and Electronics Engineering, PSG College of Technology, India
     

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A brain tumor occurs when abnormal cells form within the brain. In diagnosis of the disease medical imaging has many advantages. Many people suffer from brain tumor, it is a serious and dangerous disease. A proper diagnosis of brain tumor is provided by the medical imaging. The detection and classification of tumor from brain is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. Tumor detection and classification are very hard because of high quantity of data in MRI images. One essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision, yields with decrease in the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification include image acquisition, image preprocessing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using wiener filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co-occurrence Matrix (GLCM) features are used. In the last stage images are classified either as tumorous or non-tumorous. Classification is done using Support Vector Machine (SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy of these classifier, CNN would provide high accuracy. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using PSGIMSR (PSG Hospitals, Coimbatore) dataset and implemented using MATLAB software.

Keywords

Wiener filter, Edge Adaptive Total Variation Denoising, Gray Level Co-occurrence Matrix, Support Vector Machine, Convolutional Neural Network.
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  • Performance Analysis of SVM and Deep Learning with CNN for Brain Tumor Detection and Classification

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Authors

D. M. Mahalakshmi
Department of Electrical and Electronics Engineering, PSG College of Technology, India
S. Sumathi
Department of Electrical and Electronics Engineering, PSG College of Technology, India

Abstract


A brain tumor occurs when abnormal cells form within the brain. In diagnosis of the disease medical imaging has many advantages. Many people suffer from brain tumor, it is a serious and dangerous disease. A proper diagnosis of brain tumor is provided by the medical imaging. The detection and classification of tumor from brain is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. Tumor detection and classification are very hard because of high quantity of data in MRI images. One essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision, yields with decrease in the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification include image acquisition, image preprocessing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using wiener filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co-occurrence Matrix (GLCM) features are used. In the last stage images are classified either as tumorous or non-tumorous. Classification is done using Support Vector Machine (SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy of these classifier, CNN would provide high accuracy. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using PSGIMSR (PSG Hospitals, Coimbatore) dataset and implemented using MATLAB software.

Keywords


Wiener filter, Edge Adaptive Total Variation Denoising, Gray Level Co-occurrence Matrix, Support Vector Machine, Convolutional Neural Network.

References