Automated Categorization of Brain Tumor from MRI Using CNN features and SVM
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ORIGINAL RESEARCH
Automated Categorization of Brain Tumor from MRI Using CNN features and SVM S. Deepak1 · P. M. Ameer1 Received: 17 April 2020 / Accepted: 19 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Automated tumor characterization has a prominent role in the computer-aided diagnosis (CAD) system for the human brain. Despite being a well-studied topic, CAD of brain tumors poses severe challenges in some specific aspects. One such challenging problem is the category-based classification of brain tumors among glioma, meningioma, and pituitary tumors using magnetic resonance imaging (MRI) images. The emergence of deep learning and machine learning algorithms have addressed image classification tasks with promising results. But an associated limitation with the medical image classification is the small sizes of medical image databases. This limitation, in turn, limits the availability of medical images for training deep neural networks. To mitigate this challenge, we adopt a combination of convolutional neural network (CNN) features with support vector machine (SVM) for classification of the medical images. The fully automated system is evaluated using Figshare open dataset containing MRI images for the three types of brain tumors. CNN is designed to extract features from brain MRI images. For enhanced performance, a multiclass SVM is used with CNN features. Testing and evaluation of the integrated system followed a fivefold cross-validation procedure. The proposed model attained an overall classification accuracy of 95.82%, better than the state-of-the-art method. Extensive experiments are performed on other MRI datasets for the brain to ascertain the improved performance of the proposed system. When the amount of available training data is small, the SVM classifier is observed to perform better than the softmax classifier for the CNN features. Compared to transfer learning-based classification, the adopted strategy of CNN-SVM has lesser computations and memory requirements. Keywords Convolutional neural network · Support vector machine · Brain tumor · Computational complexity
1 Introduction Computer-aided diagnosis (CAD) of diseases has made considerable progress over the years. Improved imaging techniques, better image processing tools, advanced machine learning theories, the advent of deep learning concepts have made it possible. Automatic disease detection, disease category classification, medical image retrieval based reference, disease progress monitoring are popular research topics in medical image processing and analysis. The conventional approaches to disease detection and disease classification problems using medical images adopted various feature
* S. Deepak [email protected] P. M. Ameer [email protected] 1
National Institute of Technology, Calicut, India
extraction and classification algorithms. Finding the most suited combination of features and a classifier is considered as a challenging task. The choice of this combination often relies on
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