Abstract
The brain tumor can be created by uncontrollable increase of abnormal cells in tissue of brain and it has two kinds of tumors: one is benign and another one is malignant tumor. The benign brain tumor does not affect the adjacent normal and healthy tissue but the malignant tumor can affect the neighboring tissues of brain that can lead to the death of person. An early detection of brain tumor can be required to protect the survival of patients. Usually, the brain tumor is detected using MRI scanning method. However, the radiologists are not providing the effective tumor segmentation in MRI image due to the irregular shape of tumors and position of tumor in the brain. Accurate brain tumor segmentation is needed to locate the tumor and it is used to give the correct treatment for a patient and it provides the key to the doctor who must execute the surgery for patient. In this paper, a novel deep learning algorithm (kernel based CNN) with M-SVM is presented to segment the tumor automatically and efficiently. This presented work contains several steps that are preprocessing, feature extraction, image classification and tumor segmentation of brain. The MRI image is smoothed and enhanced by Laplacian of Gaussian filtering method (LoG) with Contrast Limited Adaptive Histrogram Equalization (CLAHE) and feature can be extracted from it based on tumor shape position, shape and surface features in brain. Consequently, the image classification is done using M-SVM depending on the selected features. From MRI image, the tumor is segmented with help of kernel based CNN method.. Experimental results of proposed method can show that this presented technique can executes brain tumor segmentation accurately reaching almost 84% in evaluation with existing algorithms.
Similar content being viewed by others
References
Lokesh, S., Kumar, P. M., Devi, M. R., Parthasarathy, P., and Gokulnath, C., An automatic tamil speech recognition system by using bidirectional recurrent neural network with self-organizing map. Neural Comput. Applic. 1–11, 2018.
Kanisha, B., Lokesh, S., Kumar, P. M., Parthasarathy, P., and Chandra Babu, G., Speech recognition with improved support vector machine using dual classifiers and cross fitness validation. Pers. Ubiquit. Comput. 1–9, 2018.
Kumar, P. M., Lokesh, S., Varatharajan, R., Babu, G. C., and Parthasarathy, P., Cloud and IoT based disease prediction and diagnosis system for healthcare using fuzzy neural classifier. Futur. Gener. Comput. Syst. 86:527–534, 2018.
Chandra, I., Sivakumar, N., Gokulnath, C. B., and Parthasarathy, P., IoT based fall detection and ambient assisted system for the elderly. Clust. Comput. 1–9, 2018.
Mathan, K., Kumar, P. M., Panchatcharam, P., Manogaran, G., and Varadharajan, R., A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Des. Autom. Embed. Syst. 1–18, 2018.
Parthasarathy, P., and Vivekanandan, S., Investigation on uric acid biosensor model for enzyme layer thickness for the application of arthritis disease diagnosis. Health Inf. Sci. Syst. 6:1–6, 2018.
Parthasarathy, P., and Vivekanandan, S., A comprehensive review on thin film-based nano-biosensor for uric acid determination: Arthritis diagnosis. World Rev. Sci. Technol. Sustain. Dev. 14(1):52–71, 2018.
Parthasarathy, P., and Vivekanandan, S., A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases. Inform. Med. Unlocked, 2018.
Varadharajan, R., Priyan, M. K., Panchatcharam, P., Vivekanandan, S., and Gunasekaran, M., A new approach for prediction of lung carcinoma using back propogation neural network with decision tree classifiers. J. Ambient Intell. Humaniz. Comput. 1–12, 2018.
Parthasarathy, P., and Vivekanandan, S., Urate crystal deposition, prevention and various diagnosis techniques of GOUT arthritis disease: A comprehensive review. Health Inf. Sci. Syst. 6(1):19, 2018.
Wang, M., Yang, J., Chen, Y., and Wang, H., The multimodal brain tumor image segmentation based on convolutional neural networks. 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), pp 336–339, 2017.
Xing, F., Xie, Y., and Yang, L., An automatic learning-based framework for robust nucleus segmentation. IEEE Trans. Med. Imaging 35(2):550–566, 2016.
Mohsen, H., El-Dahshan, E.-S. A., El-Horbaty, E.-S. M., and Salemd, A.-B. M., Classification using deep learning neural networks for brain tumors. Futur. Comput. Inf. J. 3(1):68–71, 2018.
Amiri, S., Rekik, I., and Mahjoub, M. A., Deep random forest-based learning transfer to SVM for brain tumor segmentation. 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp 297–302, 2016.
Isın, A., Direkoglu, C., and Sahc, M., Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput. Sci. 102:317–324, 2016.
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., and Erickson, B. J., Deep learning for brain MRI segmentation: State of the art and future directions. J. Digit. Imaging 30(4):449–459, 2017.
Islam, A., Reza, S. M. S., and Iftekharuddin, K. M., Multifractal texture estimation for detection and segmentation of brain tumors. IEEE Trans. Biomed. Eng. 60(11):3204–3215, 2013.
Pereira, S., Pinto, A., Alves, V., and Silva, C. A., Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5):1240–1251, 2016.
Pereira, S., Pinto, A., Alves, V., and Silva, C. A., Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI. Int. Conf. Med. Image Comput. Comput. Assist. Interv. pp 706–714, 2018.
Huang, M., Yang, W., Wu, Y., Jiang, J., Chen, W., and Feng, Q., Brain tumor segmentation based on local independent projection-based classification. IEEE Trans. Biomed. Eng. 61(10):2633–2645, 2014.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The author’s has no conflict of interest in submitting the manuscript to this journal.
Ethical Approval
This article does not contain any studies with human participants performed by any of the authors.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the Topical Collection on Image & Signal Processing
Rights and permissions
About this article
Cite this article
Thillaikkarasi, R., Saravanan, S. An Enhancement of Deep Learning Algorithm for Brain Tumor Segmentation Using Kernel Based CNN with M-SVM. J Med Syst 43, 84 (2019). https://doi.org/10.1007/s10916-019-1223-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-019-1223-7