Brain damage detection using machine learning approach

Shaikh Abdul Hannan; Manjusha Hivre; Manju Lata; Bandi Hari Krishna; Sathyasiva Sathyasiva; Muhammad Waqas Arshad

doi:10.53730/ijhs.v6nS8.13317

Authors

Shaikh Abdul Hannan Assistant Professor, Department of Computer Science and Information Technology, Albaha University, Albaha, Kingdom of Saudi Arabia
Manjusha Hivre Assistant professor, Department of Biochemistry, MGM Medical College Aurangabad MGMIHS, Navi Mumbai, Maharashtra
Manju Lata Associate Professor, Department of Zoology, M.S.J Government College, Bharatpur, Rajasth¬an
Bandi Hari Krishna
hariphysiologist@gmail.com
Assistant professor, Dept of Physiology, ACSR Govt Medical College, Nellore, Andhra Pradesh
Sathyasiva Clinical Physiotherapist, Department of Physiotherapy, Saveetha University, Saveetha Institute of Medical, Thandalam
Muhammad Waqas Arshad Research Scholar, Dept of Creative Technologies, Air University, Islamabad, Pakistan

Keywords:

brain damage, machine learning, detection, magnetic resonance imaging (MRI)

Abstract

The diagnosis of brain tumours has sparked attention in several research fields recently. Since the human body has anatomical structure by nature, finding brain tumours is an extremely laborious and time-consuming task. Cells develop quickly and uncontrollably, which causes brain tumours. It may cause death if not addressed in the beginning stages. Although there have been many substantial efforts and encouraging results in this field, precise segmentation and classification remain difficult tasks. Because of the variability in tumour location, shape, and size, detecting brain tumours is a significant difficulty. One of the most crucial problems with artificial intelligence systems is medical diagnostics using image processing and machine learning. Magnetic resonance imaging (MRI) is one of the technologies frequently used to find tumours in the brain (MRI). It provides crucial details that are employed in the process of carefully scanning the internal organisation of the human body. The variety and intricacy of brain tumours make it difficult to classify MR images. Sigma sifting, versatile limit, and detection locale are a portion of the cycles in the recommended technique for finding a brain cancer in MR pictures.

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References

AAslam, E, Khan, M & Sufyan Beg, M 2015, ‘Improved edge detection algorithm for brain tumor segmentation’, Elsevier, Second International Symposium on Computer Vision and the Internet.

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.

G. Sujatha1and K. Usha Rani, “Evaluation of Decision Tree Classifiers On tumor Datasets”, International Journal of Emerging Trends & Technology In Computer Science, 2013.

Ganesh Ram Nayak & Toran Verma 2014, ‘Brain cancer classification using back propagation neural network and principle component analysis’, International Journal of Technical Research and Applications, vol. 2, no. 4, pp. 26-31.

Gharaviri, A, Dehghan, Faramarz, Teshnelab, Mohammad & Abrishami, H 2008, ‘Comparison of neural network, ANFIS, and SVM classifiers for PVC arrhythmia detection’, Proceedings of International Conference on Machine Learning and Cybernetics, vol. 2, pp. 750-755.

Guyon, I, Weston, J, Barnhill, S & Vapnik, V 2002, ‘Gene selection for cancer classification using support vector machines’, Machine learning, vol. 46, no. 1-3, pp. 389-422.

H. Najadat, Y. Jaffal, O. Darwish, and N. Yasser. A classifier to detect abnormality in CT brain images. In The 2011 IAENG International Conference on Data Mining and Applications, pages 374–377, Mar 2011.

J Priyanka, Balwinder Singh, “A Review On Brain Tumor Detection Using Segmentation”, International Journal Of Computer Science And Mobile Computing, pg.48 – 54, Vol. 2, Issue. 7, July 2013.

Johnson DR, Guerin JB, Giannini C, Morris JM, Eckel LJ, Kaufmann TJ (2017) 2016 updates to the WHO brain tumor classification system: what the radiologist needs to know. Radiographics 37:2164–2180

Khan MA, Lali IU, Rehman A, Ishaq M, Sharif M, Saba T et al (2019) Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection. Microsc Res Tech 82:909–922

Khare, S, Gupta, N & Srivastava, V 2014, ‘Optimization technique, curve fitting and machine learning used to detect Brain Tumor in MRI’, Proceedings of IEEE International Conference on Computer Communication and Systems.

Le, T, Tran, D, Ma, W & Sharma, D 2010, 'A new support vector machine method for medical image classification', in 2010 2nd European Workshop on Visual Information Processing (EUVIP), pp. 165-170, IEEE.

M. F. Othman and M. A. M. Basri. Probabilistic neural network for brain tumor classification. In Second International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pages 136–138, Jan 2011.

Madheswaran, M & Anto, SD 2015, ‘Classification of brain MRI images using support vector machine with various Kernels’, Biomedical Research, vol. 26, no. 3, pp. 505-513.

Megha A Joshi & Prof Shah, DH 2015, ‘Survey of brain tumor detection techniques through MRI images’, American International Journal of Research in Formal, Applied and Natural Sciences, vol. 2, pp. 9-12.

Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J et al (2014) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34:1993–2024

N. N. Gopal and M. Karnan. Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques. In IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pages 1–4, Dec 2010.

R.Siva Kumar and M. Karnan “Review of MRI Image Classification Techniques”, International Journal of Research Studies In Computer Science and Engineering, PP 21-28, Volume 1, Issue 1May 2014.

Sharif M, Amin J, Nisar MW, Anjum MA, Muhammad N, Shad SA (2020) A unified patch based method for brain tumor detection using features fusion. Cogn Syst Res 59:273–286

Sharif MI, Li JP, Naz J, Rashid I (2020) A comprehensive review on multi-organs tumor detection based on machine learning. Pattern Recogn Lett 131:30–37

Vaishnavee, KB & Amshakala, K 2015, ‘An automated MRI brain image segmentation and tumor detection using SOM-clustering and Proximal Support Vector Machine classifier’, IEEE International Conference on Engineering and Technology (ICETECH), pp. 1-6.

Veeranki S. R, Varshney M. (2022). Intelligent Techniques and Comparative Performance Analysis of Liver Disease Prediction, International Journal of Mechanical Engineering, 7(1), 489-503. https://kalaharijournals.com/ijme-vol7-issue-jan2022part2.php,

Yudong Zhanga, Zhengchao Dongb and Lenan Wua “A Hybrid Method For MRI Brain Image Classification”, Volume 38, Issue 8, Pages 10049–10053, August 2011.

Yuedong Song & Pietro Liò 2010, 'A new approach for epileptic seizure detection: Sample entropy based feature extraction and extreme learning machine', Journal of Biomedical Science and Engineering, vol. 3, pp. 556-567.

Zacharaki, EI, Wang, S, Chawla, S, Yoo, DS, Wolf, R, Melhem, ER & Davatzikos, C 2009, ‘Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme’, Magnetic Resonance in Medicine, vol. 62, pp. 1609-1618.

Zhao, X, Yihong, W, Song, G, Li, Z, Zhang, Y & Fan, Y 2018, ‘A deep learning model integrating FCNNs and CRFs for brain tumor segmentation’, Med. Image Anal. vol. 43, pp. 98-111.