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CNN and RF Based Classification of Brain Tumors in MR Neurological Images

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Computer Vision and Image Processing (CVIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1147))

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Abstract

Identification of a brain tumor at the early stage is of significant importance to provide proper diagnosis and treatment according to that. This is very difficult to estimate the accurate information regarding the tumor position, shape and size and it requires a lot of years of experience. So, computer-aided diagnostic system is required to assist the radiologists correctly. In this paper, random forest (RF) and convolutional neural network (CNN) is used to classify multi-class brain tumor dataset having 3064 T1-weighted contrast-enhanced images. Firstly, features like gray level co-occurrence matrix (GLCM), local binary pattern (LBP) and shape features are computed for a particular region of interest (SROI’s). Further, classification is performed with three different types of approaches: i.e. RF, RF with principal component analysis (RF-PCA), and RF-PCA with random selection. In the second experiment, different types of optimization algorithm in the CNN model such as adaptive moment estimation (Adam), stochastic gradient descent with momentum (SGDM) and root mean square propagation (RMSProp) are utilized and compared with the RF based approaches. To assess the performance of the presented classification methods, two different experiments have been performed. In the first experiment, both RF and RF-PCA with random selection approaches reported the testing accuracy of \(91.95\%\). Also, the corresponding validation accuracies of both of these approaches are \(93.29\%\) and \(89.93\%\), respectively. In the second experiment, SGDM optimization algorithm reported a significant testing and validation accuracy of \(87.9\%\) and \(89.9\%\), respectively.

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Authors and Affiliations

  1. Department of ECE, Visvesvaraya National Institute of Technology, Nagpur, India

    Vishlavath Saraswathi, Ankush D. Jamthikar & Deep Gupta

Authors
  1. Vishlavath Saraswathi
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  2. Ankush D. Jamthikar
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  3. Deep Gupta
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Correspondence to Deep Gupta .

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Editors and Affiliations

  1. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Neeta Nain

  2. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Santosh Kumar Vipparthi

  3. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

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Saraswathi, V., Jamthikar, A.D., Gupta, D. (2020). CNN and RF Based Classification of Brain Tumors in MR Neurological Images. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1147. Springer, Singapore. https://doi.org/10.1007/978-981-15-4015-8_11

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  • DOI: https://doi.org/10.1007/978-981-15-4015-8_11

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  • Online ISBN: 978-981-15-4015-8

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