Research Article | OPEN ACCESS
Brain Tumor Detection and Classification Using Deep Learning Classifier on MRI Images
1V.P. Gladis Pushpa Rathi and 2S. Palani
1Department of Computer Science and Engineering, Sudharsan Engineering College
2Sudharsan Engineering College, Sathiyamangalam, Pudukkottai, India
Research Journal of Applied Sciences, Engineering and Technology 2015 2:177-187
Received: October 10, 2014 | Accepted: December 18, 2014 | Published: May 20, 2015
Abstract
Magnetic Resonance Imaging (MRI) has become an effective tool for clinical research in recent years and has found itself in applications such as brain tumour detection. In this study, tumor classification using multiple kernel-based probabilistic clustering and deep learning classifier is proposed. The proposed technique consists of three modules, namely segmentation module, feature extraction module and classification module. Initially, the MRI image is pre-processed to make it fit for segmentation and de-noising process is carried out using median filter. Then, pre-processed image is segmented using Multiple Kernel based Probabilistic Clustering (MKPC). Subsequently, features are extracted for every segment based on the shape, texture and intensity. After features extraction, important features will be selected using Linear Discriminant Analysis (LDA) for classification purpose. Finally, deep learning classifier is employed for classification into tumor or non-tumor. The proposed technique is evaluated using sensitivity, specificity and accuracy. The proposed technique results are also compared with existing technique which uses Feed-Forward Back Propagation Network (FFBN). The proposed technique achieved an average sensitivity, specificity and accuracy of 0.88, 0.80 and 0.83, respectively with the highest values as about 1, 0.85 and 0.94. Improved results show the efficiency of the proposed technique.
Keywords:
Deep learning classifier, Linear Discriminant Analysis (LDA), MRI image segmentation, Multiple Kernel based Probabilistic Clustering (MKPC), shape and texture based features , tumour detection,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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