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A Novel Technique for Brain Cancer Image Classification and Segmentation


Affiliations
1 Department of Computer Science and Engineering, Chandigarh University, Gharuan, Punjab, 140413, India
 

The primary causes of brain lesions are abnormalities in brain cells. Tumor develops in the brain as a result of these aberrant lesions. MRI and CT scans are two examples of medical imaging (CT) are the two different techniques for brain imaging scanning. The interior regions of the brain are scanned using MR imaging in this research project. There are two types of aberrant lesions in the brain: benign and malignant. Benign lesions can be treated with radiation therapy, whereas malignant lesions require adequate surgery performed by a radiologist with expertise. Uncontrolled cell proliferation in any area of the body is referred to as a tumor. Tumor come in a variety of forms, has unique traits, and call for a range of therapies. Brain tumors are a current problem. Malignant or metastatic brain tumors and primary brain tumors are two different categories. The metastatic or malignant tumors start as a cancer somewhere else in the body before spreading into the brain region, but the primary tumors start in the brain and have a tendency to stay there. The automatic tumor detection and segmentation method has therefore. In order to accurately classify and identify the tumor sections, three brain tumor segmentation methods are proposed in this study. First, a powerful brain tumor segmentation method is used by merging Convolution Neural Networks (CNN) and Multi Kernel K Means Clustering and Network (MKKMC). The proposed CNN-MKKMC technique uses the CNN algorithm to classify MR images into normal, IV, and abnormal categories. The next step is to separate the brain tumor from the aberrant brain imaging using the MKKMC algorithm. The accuracy, sensitivity, and specificity of the proposed CNN-MKKMC algorithm are assessed visually as well as objectively in comparison to the currently used segmentation techniques. The experimental findings show that the suggested CNNMKKMC technique produces greater segmentation accuracy for brain tumours while requiring less time. Both clinical datasets and publically accessible open access datasets are used to test the meningioma brain tumour detection approach.

Keywords

Neural Network, Deep Learning Neural Network, Convolution Neural Network, Multi Kernel K Means Clustering and Network, Multi Kernel K Means Clustering and Network.
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  • Abdel-Maksoud Eman, Elmogy, M & Al-Awadi, R 2015, ‘Brain tumor segmentation based on a hybrid clustering technique’, Egyptian InformaticsJournal, vol. 16, no. 1, pp. 71-81.

  • Aghalari, M., Aghagolzadeh, A. and& Ezoji, M., 2021., 'Brain tumor image segmentation via asymmetric/symmetric UNet based on two- pathway-residual blocks.', Biomedical Signal Processing and Control, vol. 69, P. 102841.

  • Aparna, M 2016, ‘Brain tumor segmentation and classification using modified FCM and SVM classifier’, International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 4,pp. 73-76.

  • Ari, A & Hanbay, D 2018, ‘Deep learning based brain tumor classification and detection system’, Turkish Journal of Electrical Engineering &Computer Sciences, vol. 26, no. 5, pp. 2275-2286.

  • Athira, VS 2015, ‘Brain tumor detection and segmentation in MR images using GLCM and AdaBoost classifier’, IJSRSET, vol. 1, no. 3, pp. 142-146.

  • Bauer, S, May, C, Dionysiou, D, Stamatakos, G, Buchler, P & Reyes, M 2012, ‘Multiscale modeling for image analysis of brain tumor studies’, IEEE Transactions on Biomedical Engineering, vol. 59, no. 1,pp. 25-29.

  • Cabria, I & Gondra, I 2017, ‘MRI segmentation fusion for brain tumor detection’, Inform Fusion, vol. 36, pp. 1-9.

  • Celik, T 2012, ‘Two-dimensional histogram equalization and contrast enhancement’, Pattern Recognition, vol. 45, no. 10, pp. 3810-3824.

  • Chang, J, Zhang, L, Gu, N, Zhang, X, Ye, M, Yin, R & Meng, Q 2019,‘A mix-pooling CNN architecture with FCRF for brain tumor segmentation’, Journal of Visual Communication and Image Representation, vol. 58, pp. 316-322.

  • Chaplot, S, Patnaik, LM & Jagannathan, NR 2006, ‘Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network’, Biomedical Signal Processing and Control, vol. 1, no. 1, pp. 86-92.

  • Cheng, J, Huang, W, Cao, S, Yang, R, Yang, W, Yun, Z, Wang, Z & Feng, Q 2015, ‘Enhanced performance of brain tumor classification via tumor region augmentation and partition’, PloS One, vol. 10, no. 10, pp. 1-13

  • Danaei, G, Vander Hoorn, S, Lopez, AD, Murray, CJ & Ezzati, M 2005, ‘Causes of cancer in the world: Comparative risk assessment of nine behavioural and environmental risk factors’, The Lancet, vol. 366,no. 9499, pp. 1784-1793

  • Devkota, B, Alsadoon, A, Prasad, PWC, Singh, AK & Elchouemi, A 2018, ‘Image segmentation for early stage brain tumor detection using mathematical morphological reconstruction’, Procedia Computer Science, vol. 125, pp. 115-123.

  • Dhanve, V & Kumar, M 2017, ‘Detection of brain tumor using k-means segmentation based on object labeling algorithm’, in IEEE International Conference on Power, Control, Signals and instrumentation Engineering (ICPCSI), pp. 944-951.

  • Dong, H, Yang, G, Liu, F, Mo, Y & Guo, Y 2017, ‘Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks’, in Annual Conference on Medical Image Understanding and Analysis, Springer. pp. 506-517.

  • El-Dahshan, ESA, Mohsen, HM, Revett, K & Salem, ABM 2014, ‘Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm’, Expert systems with Applications, vol. 41, no. 11,pp. 5526-5545

  • Eser Serta, Fatih Özyurt & Akif Dogantekinc 2019, ‘A new approach for brain tumor diagnosis system: Single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network’, Medical Hypotheses, vol. 133, pp. 1-9

  • Fatih Ozyurt, Eser Sert, Engin Avci & Esin Dogantekin 2019, ‘Brain tumor detection based on convolutional neural network with neutrosophic expert maximum fuzzy sure entropy’, Measurement, vol. 147, P. 106830

  • Gordillo, N, Montseny, E & Sobrevilla, P 2013, ‘State of the art survey on MRI brain tumor segmentation’, Magnetic Resonance Imaging, vol. 31,no. 8, pp. 1426-1438.

  • Hamamci, A, Kucuk, N, Karaman, K, Engin, K & Unal, G 2012, ‘Tumor- cut: Segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications’, IEEE Transactions on Medical Imaging,vol. 31, no. 3, pp. 790-804.

  • Havaei, M, Davy, A, Warde-Farley, D, Biard, A, Courville, A, Bengio, Y, Pal, C, Jodoin, PM & Larochelle, H 2017, ‘Brain tumor segmentation with deep neural networks’, Medical Image Analysis, vol. 35, pp. 18-31.

  • Herald Anantha Rufus, N & Selvathi, D 2018, ‘Performance analysis of brain tissues and tumor detection and grading system using ANFIS classifier’, International Journal of Imaging Systems and Technology, vol. 28, no. 2, pp. 77-85.

  • Hikmat Khan, PM, Shah, MA & Shah 2020, ‘Cascading handcrafted features and convolutional neural network for IoT-enabled brain tumor segmentation’, Computer Communications, Available from: .

  • Huang, Z, Zhao, Y, Liu, Y & Song, G 2021, 'GCAUNet: A group cross- channel attention residual UNet for slice based brain tumor segmentation.', Biomedical Signal Processing and Control, vol. 70,P. 102958.

  • Islam, AS, Reza, MS & Iftekharuddin, KM 2013, ‘Multifractal texture estimation for detection and segmentation of brain tumors’, IEEETransactions on Biomedical Engineering, vol. 60, no. 11, pp. 3204-3215

  • Johnpeter, JH & Ponnuchamy, T 2019, ‘Computer aided automated detection and classification of brain tumors using CANFIS classification method’, International Journal of Imaging Systems and Technology, vol. 29, no. 4, pp. 431-438.

  • Kesav, N & Jibukumar, MG 2021, 'Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN.', Journal of King Saud University-Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2021.05.008.

  • Long, J, Shelhamer, E & Darrell, T 2015, ‘Fully convolutional networks for semantic segmentation’, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431-3440.

  • Mallikarjuna, E 2015, ‘Brain tumor detection using segmentation based object labelling algorithm’, Journal of Computation in Biosciences and Engineering, vol. 3, pp. 1-5.

  • Malviya, MAM & Joshi, AS 2014, ‘Review on automatic brain tumor detection based on gabor wavelet’, International Journal of Engineering Research & Technology, vol. 3, no. 1, pp. 53-5.

  • Mamta Mittal, Lalit Mohan Goyal, Sumit Kaur, Iqbaldeep Kaur, Amit Verma & Jude Hemanth 2019, ‘Deep learning based enhanced tumor segmentation approach for MR brain images’, Applied Soft Computing Journal, vol. 78, pp. 346-354.

  • Milica M Badža & Barjaktarović, MČ 2020, ‘Classification of brain tumorsfrom MRI images using a convolutional neural network’, Applied Sciences, vol. 10, no. 6, pp. 1-13

  • Shahvaran, Z, Kazemi, K, Fouladivanda, M, Helfroush, MS, Godefroy, O & Aarabi, A 2021, 'Morphological active contour model for automatic brain tumor extraction from multimodal magnetic resonanceimages', Journal of Neuroscience Methods, vol. 362, P. 109296.

  • Shubhangi Nemaa Akshay Dudhane, Subrahmanyam Muralaa & Srivatsava Naidu 2020, ‘RescueNet: An unpaired GAN for brain tumor segmentation’, Biomedical Signal Processing and Control, vol. 55,P. 101641.

  • Swe Zin 2016, ‘Brain tumor detection and segmentation using watershed segmentation and morphological operation’, IJRET: International Journal of Research in Engineering and Technology, vol. 3, no. 3, pp. 367-374.


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  • A Novel Technique for Brain Cancer Image Classification and Segmentation

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Authors

Kumud Sachdeva
Department of Computer Science and Engineering, Chandigarh University, Gharuan, Punjab, 140413, India
Amandeep Kaur
Department of Computer Science and Engineering, Chandigarh University, Gharuan, Punjab, 140413, India
Rajan Sachdeva
Department of Computer Science and Engineering, Chandigarh University, Gharuan, Punjab, 140413, India

Abstract


The primary causes of brain lesions are abnormalities in brain cells. Tumor develops in the brain as a result of these aberrant lesions. MRI and CT scans are two examples of medical imaging (CT) are the two different techniques for brain imaging scanning. The interior regions of the brain are scanned using MR imaging in this research project. There are two types of aberrant lesions in the brain: benign and malignant. Benign lesions can be treated with radiation therapy, whereas malignant lesions require adequate surgery performed by a radiologist with expertise. Uncontrolled cell proliferation in any area of the body is referred to as a tumor. Tumor come in a variety of forms, has unique traits, and call for a range of therapies. Brain tumors are a current problem. Malignant or metastatic brain tumors and primary brain tumors are two different categories. The metastatic or malignant tumors start as a cancer somewhere else in the body before spreading into the brain region, but the primary tumors start in the brain and have a tendency to stay there. The automatic tumor detection and segmentation method has therefore. In order to accurately classify and identify the tumor sections, three brain tumor segmentation methods are proposed in this study. First, a powerful brain tumor segmentation method is used by merging Convolution Neural Networks (CNN) and Multi Kernel K Means Clustering and Network (MKKMC). The proposed CNN-MKKMC technique uses the CNN algorithm to classify MR images into normal, IV, and abnormal categories. The next step is to separate the brain tumor from the aberrant brain imaging using the MKKMC algorithm. The accuracy, sensitivity, and specificity of the proposed CNN-MKKMC algorithm are assessed visually as well as objectively in comparison to the currently used segmentation techniques. The experimental findings show that the suggested CNNMKKMC technique produces greater segmentation accuracy for brain tumours while requiring less time. Both clinical datasets and publically accessible open access datasets are used to test the meningioma brain tumour detection approach.

Keywords


Neural Network, Deep Learning Neural Network, Convolution Neural Network, Multi Kernel K Means Clustering and Network, Multi Kernel K Means Clustering and Network.

References