Diagnosis of Brain Hemorrhage Using Artificial Neural Network

V. Davis¹ , S. Devane²

¹ Electronics and Telecommunication, Mumbai University, Mumbai, India.
² Electronics and Telecommunication, Mumbai University, Mumbai, India.

Correspondence should be addressed to: satish@dmce.ac.in.

Section:Research Paper, Product Type: Journal
Vol.5 , Issue.1 , pp.20-23, Apr-2017

Online published on Apr 30, 2017

Copyright © V. Davis, S. Devane . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Brain hemorrhage is a type of stroke which is caused due to bursting of artery in the brain and thus causing bleeding in the surrounding tissues. The major technique which is used for diagnosis of brain hemorrhage is through Computed Tomography (CT) scan. This dissertation investigates the possibility of diagnosing brain hemorrhage using an image segmentation of CT scan images using watershed algorithm and using the inputs extracted from the brain CT image to an artificial neural network for classification. The output generated as the type of brain hemorrhages, can be used to verify diagnosis also as a learning tool for trainee radiologists to minimize errors.

Key-Words / Index Term :
Brain hemorrhage, Intracerebral Hemorrhage, Subdural Hemorrhage, Extradural Hemorrhage, Subarachnoid Hemorrhage, Computed Tomography, X-rays

References :

[1] R. Ganesan, S. Radhakrishnan, “Segmentation of Computed Tomography Brain Images Using Genetic Algorithm�, International Journal of Soft Computing ,Vol.4, Issue.4, pp.157-161, 2009.
Google Scholar

[2] D. Moitra, R. Mandal , “Review of Brain Tumor Detection using Pattern Recognition Techniques�, International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.121-123, 2017.
Google Scholar

[3] K. Sarkar, R.K. Mandal, A. Mandal, S. Sarkar, "Least Centre Distance Based MAXNET Architecture to Obtain Threshold for Brain Tumor Edema Segmentation From FLAIR MRI", International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.112-120, 2017.
Google Scholar

[4] MM. Kyaw, “Computer Aided Detection system For Hemorrhage Contained Region�, International Journal of Computational Science and Information Technology (IJCSITY), vol.1, Issue.1, pp.1-16, 2013.
Google Scholar

[5] AH. Ali, SI. Abdulsalam, IS. Nema, “Detection And Segmentation of Hemorrhage stroke Using Textural Analysis on Brain CT images�, International Journal of Soft Computing and Engineering, Vol.5, Issue.1, pp.11-14, 2015.
Google Scholar

[6] V. Vani, M.K. Geetha , “Automatic Tumor Classification of Brain MRI Images�, International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.144-151, 2016.
Google Scholar

[7] Z. Ying,R. Naidu, C.R. Crowford, “Dual Energy Computed Tomography For Exclusive Detection�, Journal of X-ray Science and Technology, Vol.14, Issue.1, pp.235-256, 2006
Google Scholar

[8] M. Chawla, “A Method for Automatic Detection and Classification of Stroke from Brain CT Images�, 31stAnnual International Conference of the IEEE EMBS, Minneapolis, pp. 3581-3584, 2009..
Google Scholar

[9] CA. Devi, S.P. Rajagopala, “Brain Stroke Classification Based on Multilayer Perceptron Using Watershed Segment and Gabor Filter�, Journal of Theoretical and Applied Information Technology, Vol.56, Issue.3, pp. 410-416, 2013.
Google Scholar

[10] Anju Bala, “An Improved Watershed Image Segmentation Technique using MATLAB�, International Journal of Scientific and Enginnering Research, Vol.3, Issue.6, pp.1-8, 2012.
Google Scholar

[11] D. Pham, C. Xu, J. L. Prince, “A Survey of Current Methods in Medical Image Segmentation", Annual Review of Biomedical Engineering, Vol.2, Issue.1, pp.315-37, 2000.
Google Scholar

[12] DK. Kharat, Pradyumna, MB. Nagori, “Brain Tumor Classification Using Neural Network Based Methods�, International Journal of Computer Science and Informatics, Vol.1, Issue.4, pp.2231-5292, 2012.
Google Scholar

[13] S. Fatima, M. Sridevi, S. Naaz, K. Anjum, “Diagnosis and Classification of Brain Hemorrhage Using CAD System�, Indian Journal of Scientific Research, Vol.12, Issue.1, pp.121-125,2015.
Google Scholar