Convolutional Neural Network-Based MRI Brain Tumor Classification System
Abstract
A brain tumor, the cause of more death rates among all cancers, is diagnosed using uncontrollable cell growth and abnormal brain cell partitioning. The recent progress in Deep Learning (DL) neural network aids the health service in medical image diagnosing. The visual learning of image recognition may result in fault detection and that can be solved using machine learning. The Convolutional Neural Network (CNN) model is used in our study to categorize distinct brain tumor types. There are three main phases namely; image pre-processing, feature extraction and classification. In pre-processing stage, the image processing is done by edge detection and cropping of MRI brain images. Then feature extraction employing the Transfer Learning (TL) approach is followed by CNN model classifier layers for the classification of brain tumor images. The experimental results demonstrate that our model is extremely effective at minimal computing power with less complexity. In the performance comparison of our suggested CNN model with VGG16, achieves greater accuracy even with less dataset.Downloads
References
NBTS, National Brain Tumor Society: Quick brain tumor facts, 2020. Available from: https://braintumor.org/brain-tumor-information/brain-tumor-facts/.
Cancer. Net, Brain Tumor: Statistics, 2020. Available from: https://www.cancer.net/cancer- types/brain-tumor/statistics.
NHS, National Health Service: Brain Tumors, 2020. Available from: https://www.nhs.uk/conditions/brain-tumours/.
S. Basheera and M. S. S. Ram, “Classification of brain tumors using deep features extracted using CNN,” J. Phys., Vol.1172, No.1, p. 012016, (2019).
M. Sajjad, S. Khan, M. Khan, W. Wu, A. Ullah and S. W. Baik, “Multi-grade brain tumor classification using deep CNN with extensive data augmentation,” J. Comput. Sci., Vol.30, pp. 174–182, (2019)
R. Carlo, C. Renato, C. Giuseppe, U. Lorenzo, I. Giovanni and S. Domenico, “Distinguishing functional from non-functional pituitary macro adenomas with a machine learning analysis,” Mediterranean Conference on Medical and Biological Engineering and Computing, Springer, pp.1822–1829, 2019.
S. Khawaldeh, U. Pervaiz, A. Rafiq and R. Alkhawaldeh, “Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks,” J. Appl. Sci., Vol.8, No.1, p. 27, (2018).
N. Abiwinanda, M. Hanif, S. Hesaputra, A. Handayani and T. R. Mengko, “Brain tumor classification using convolutional neural network,” World Congress on Medical Physics and Biomedical Engineering, Springer, Singapore, pp. 183-189, 2019.
S. Das, R. Aranya and N. Labiba, “Brain tumor classification using convolutional neural network,” 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1-5, 2019.
V. Romeo, R. Cuocolo, C. Ricciardi, L. Ugga, S. Cocozza, F. Verde, et al., Prediction of tumor grade and nodal status in oropharyngeal and oral cavity squamous cell carcinoma using a radiomic approach, Anticancer Res., Vol.40, No.1, pp. 271–280, (2020).
M. Talo, U. B. Baloglu, O. Yldrm and U. R. Acharya, “Application of deep transfer learning for automated brain abnormality classification using MRI images,” Cognitive Systems Research, Vol.54, pp. 176–188, (2019).
A. Rehman, S. Naz, M. I. Razzak, F. Akram and M. Imran, “A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning,” Circuits Syst. Signal Processing, Vol.39, No.2, pp. 757–775, (2020).
A. Cinar and M. Yldrm, “Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture,” Med. Hypotheses, Vol.139, p. 109684, (2020).
N. Chakrabarty, “Brain MRI images dataset for brain tumor detection,” Kaggle, 2019. Available from: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection.
J. F. Canny, “Canny Edge Detection,” Open Source Computer Vision, Open CV. Available from: https://docs.opencv.org/trunk/da/d22/tutorial py canny.html.
C. Shorten and T. M. Khoshgoftaa, “A survey on image data augmentation for deep learning,” J. Big Data, Vol.6, No.1, pp. 1-48, (2019).
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors need to sign following agreement with International Journal of MC Square Scientific Research before publishing their articles:
- Authors need to return copyright form to Journal Editor-in-chief to proceed their articles for publication. Meantime, the journal licensed under a Creative Commons Attribution License, which permits other user to distribute the work with an acknowledgement of the authors for International Journal of MC Square Scientific Research.
- Authors are also able to share their separate, additional contractual arrangements for the non-restricted contribution of the journal with an acknowledgement of publication in International Journal of MC Square Scientific Research.
- Authors are allowed and encouraged to share their work during the submission process for increasing citation and exploring to increase the paper availability in worldwide way. The Effect of Open Access.
