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Paper Title : GENERATIVE ADVERSARIAL MODEL NETWORK FOR EFFECTED AREA MONITORING IMAGES BASED ON CT PATHOLOGICAL IMAGES ANALYSIS OF BRAIN

ISSN : 2395-1303
Year of Publication : 2021
10.29126/23951303/IJET-V7I5P3

Authors: -Dr. D.J. Samatha Naidu , B. Ramakrishna

         


Abstract
- Machine learning is used to analyze medical datasets. Recently, deep learning technology and abnormal patients. Then this abnormal patient data is stored into a two-dimensional array and passed to get results. The experimental result shows that the classification model achieves the best accuracy. Through experimental results, we found that deep learning models are not only used in non-medical images but also give high accurate results on medical image diagnosis, especially in brain stroke detection. gaining success in many domains including computer vision and convolutional neural networks, image recognition, natural language processing, and especially in the medical field of radiology. This project helps to diagnose brain stroke from MRI using CNN and deep learning models. The proposed methodology is to classify brain stroke MRI images into normal and abnormal images. In particular, three types of convolutional neural networks that are ResNet, MobileNet, and VGG16 are used. For classification, we passed pre-processed stroke MRI for training, trained all layers, and classify normal.

Reference
[1] Q. Zhu, B. Du, and P. Yan, ‘‘Boundary-weighted domain adaptive neural network for prostate MR image segmentation,’’ IEEE Trans. Med. Imag., vol. 39, no. 3, pp. 753–763, Mar. 2020. [2] Q. Zhu, B. Du, P. Yan, H. Lu, and L. Zhang, ‘‘Shape prior constrained PSO model for bladder wall MRI segmentation,’’ Neurocomputing, vol. 294, pp. 19–28, Jun. 2018. [3] Q. Zhu, B. Du, B. Turkbey, P. Choyke, and P. Yan, ‘‘Exploiting interslice correlation for MRI prostate image segmentation, from recursive neural networks aspect,’’ Complexity, vol. 2018, pp. 1–10, Feb. 2018. [4] K. Kranthi Kumar and T. V. Gopal, ‘‘A novel approach to self order feature reweighting in CBIR to reduce semantic gap using relevance feedback,’’ in Proc. Int. Conf. Circuits, Power Comput. Technol. (ICCPCT), Mar. 2014, pp. 1437– 1442. [5] R. Ashraf, K. B. Bajwa, and T. Mahmood, ‘‘Content-based image retrieval by exploring bandletized regions through support vector machines.,’’ J. Inf. Sci. Eng., vol. 32, no. 2, pp. 245–269, 2016. [6] J. Wan, D. Wang, S. C. H. Hoi, P. Wu, J. Zhu, Y. Zhang, and J. Li, ‘‘Deep learning for content-based image retrieval: A comprehensive study,’’ in Proc. ACM Int. Conf. Multimedia (MM), 2014, pp. 157–166. [7] F. Shaukat, G. Raja, R. Ashraf, S. Khalid, M. Ahmad, and A. Ali, ‘‘Artificial neural network based classification of lung nodules in ct images using intensity, shape and texture features,’’ J. Ambient Intell. Humanized Comput., vol. 10, no. 10, pp. 4135–4149, 2019. [8] A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, ‘‘Large-scale video classification with convolutional neural networks,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2014, pp. 1725–1732. [9] G. Wu, W. Lu, G. Gao, C. Zhao, and J. Liu, ‘‘Regional deep learning model for visual tracking,’’ Neurocomputing, vol. 175, pp. 310–323, Jan. 2016 [10] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-R. Mohamed, N. Jaitly, A. Senior, V
Keywords
— Brain stroke, deep learning, convolutional neural network.