Detection of Brain Tumour Using Segmentation and Classification
Article Sidebar
Main Article Content
Abstract
Brain Tumours can be taken through Magnetic Resonance Imaging (MRI).The MR images can be used to identify the tumours in brain through various learning techniques. The MR images can give the information more than Computed Tomography (CT) images and it is not harmful to human because CT image are taken through radiation whereas MRI are taken through magnetic field and radio waves. So preference is more for MR images. In these the machine learning based segmentation of tumour from MR images can be determined. Among that the Clustering based segmentation is performed. Various classification techniques are performed to determine the types of tumour are also performed, from that the optimized classification technique can be determined. In these classification techniques such as support vector machnine,linear svm,nu-svm classifier. The integrated clustering techniques have produced tremendous output images with minimal filtering process to remove the background scene. The optimized clustering can be find by the highest accuracy for those detected tumours through various techniques. There are 1000 MR Brain images with different type of tumours can be taken as the dataset, for these the clustering and classification techniques are performed.
Article Details
References
Mohsen, H., El-Dahshan, E. S. A., El-Horbaty, E. S. M., & Salem, A. B. M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1), 68-71.
Gumaei, A., Hassan, M. M., Hassan, M. R., Alelaiwi, A., & Fortino, G. (2019). A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access, 7, 36266-36273.
Khan, M. A., Ashraf, I., Alhaisoni, M., Damaševičius, R., Scherer, R., Rehman, A., & Bukhari, S. A. C. (2020). Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. Diagnostics, 10(8), 565.
Siar, M., & Teshnehlab, M. (2019, October). Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm. In 2019 9th international conference on computer and knowledge engineering (ICCKE) (pp. 363-368). IEEE.
Zhou, M., Scott, J., Chaudhury, B., Hall, L., Goldgof, D., Yeom, K. W., ... & Gatenby, R. (2018). Radiomics In Brain Tumor: Image Assessment, Quantitative Feature Descriptors, And Machine-Learning Approaches. American Journal of Neuroradiology, 39(2), 208-216.
Rehman, A., Naz, S., Razzak, M. I., Akram, F., & Imran, M. (2020). A Deep Learning-Based Framework For Automatic Brain Tumors Classification Using Transfer Learning. Circuits, Systems, and Signal Processing, 39(2), 757-775.
Sharma, K., Kaur, A., & Gujral, S. (2014). Brain tumor detection based on machine learning algorithms. International Journal of Computer Applications, 103(1).
Sun, L., Zhang, S., Chen, H., & Luo, L. (2019). Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning. Frontiers in neuroscience, 13, 810.
Kao, P. Y., Ngo, T., Zhang, A., Chen, J. W., & Manjunath, B. S. (2018, September). Brain tumor segmentation and tractographic feature extraction from structural MR images for overall survival prediction. In International MICCAI Brainlesion Workshop (pp. 128-141). Springer, Cham.
Sharif, M. I., Li, J. P., Khan, M. A., & Saleem, M. A. (2020). Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognition Letters, 129, 181-189.
Zacharaki, E. I., Wang, S., Chawla, S., Soo Yoo, D., Wolf, R., Melhem, E. R., & Davatzikos, C. (2009). Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 62(6), 1609-1618.
Varuna Shree, N., & Kumar, T. N. R. (2018). Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain informatics, 5(1), 23-30.