|Search by item||HOME > Access full text > Search by item|
JBE, vol. 23, no. 5, pp.669-681, September, 2018
Deep Learning Algorithm to Identify Cancer Pictures
Young-Min Seo and Jong-Ki Han
C.A E-mail: email@example.com
CNN (Convolution Neural Network) is one of the most important techniques to identify the kind of objects in the captured pictures. Whereas the conventional models have been used for low resolution images, the technique to recognize the high resolution images becomes crucial in the field of artificial intelligence. In this paper, we proposed an efficient CNN model based on dilated convolution and thresholding techniques to increase the recognition ratio and to decrease the computational complexity. The simulation results show that the proposed algorithm outperforms the conventional method and the thresholding technique enhances the performance of the proposed model.
Keyword: CNN, Dilated Convolution, Image Recognition
 Andre Esteva1, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun, “Dermatologist-level classification of skin cancer with deep neural networks”, Nature, 542, pp. 115-118, Feb., 2017. [DOI: 10.1038/nature21056]
 Fabio Alexandre Spanhol, Luiz S. Oliveira, Caroline Petitjean, “Breast cancer histopathological image classification using Convolutional Neural Networks”, 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2560-2567, July, 2016. [DOI: 10.1109/IJCNN. 2016.7727519]
 Korsuk Sirinukunwattana, Shan E Ahmed Raza, Yee-Wah Tsang, “Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images”, IEEE transactions on Medical Imaging, Vol. 35, Issue 5, pp. 1196-1206, May 2016. [DOI: 10.1109/TMI.2016.2525803]
 Henschen, D, “IBM's Watson could be healthcare game changer.” November, 2013. Retrieved from http://www.informationweek.com/ software/information-management/ibms-watson-could-be-healthcare-game-changer/d/d-id/1108608
 Dorrier, J, “Exponential medicine: Deep learning AI better than your doctor at finding cancer.”, November, 2015. Retrieved from https:// singularityhub.com/2015/11/11/exponential-medicine-deep-learning-ai-better-than-your-doctor-at-finding-cancer/#sm. 0001ivo53nz3nd33wx01kpv5qtzbs
 Alex Krizhevsky, “Convolutional Deep Belief Networks on CIFAR- 10”, pp. 1-9, 2010, retrieved from https://www.cs.toronto.edu/~kriz/ conv-cifar10-aug2010.pdf
 Aditya Golatkar, Deepak Anand, Amit Sethi, “Classification of Breast Cancer Histology using Deep Learning”, International Conference Image Analysis and Recognition, Springer, pp. 837-844, June, 2018. [DOI https://doi.org/10.1007/978-3-319-93000-8_95]
 Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, “Going Deeper with Convolutions”, Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1-9, June 2015. [DOI: 10.1109/CVPR.2015.7298594]
 Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, Zbigniew Wojna, “Rethinking the Inception Architecture for Computer Vision”, Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2818-2826, 2016. [DOI: 10.1109/CVPR. 2016.308]
 C Szegedy, S Ioffe, V Vanhoucke, AA Alemi, “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning”, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 4278 – 4284, 2017.
 Kim, Kwang-Baek, “Detection and Recognition of Uterine Cervical Carcinoma Cells in Pap Smear Using Kapur Method and Morphological Features”, The Journal of the Korea Information and Communications Society, 11th Issue, No. 10, pp. 1992-1998, October, 2007.
 Krizhevsky, Alex, and Geoffrey Hinton, “Learning Multiple Layers of Features from Tiny Images”, Technical report, University of Toronto, Vol. 1, No.4, pp.7, April, 2009.
 Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille, “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs”, Proceedings of ICLR, pp. 1-14, May, 2015
 G Fu, C Liu, R Zhou, T Sun, Q Zhang, “Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network”, Remote Sensing, vol. 9, Issue 5, pp.1-21, May, 2017. [doi:10.3390/rs9050498]
 Vincent Dumoulin and Francesco Visin, "A guide to convolution arithmetic for deep learning", arXiv 2016, arXiv:1603.07285, pp. 1-31, March 2016.
Wuzhen Shi, Feng Jiang, Debin Zhao, “Single image super-resolution with dilated convolution based multi-scale information learning inception module”, 2017 IEEE International Conference on Image Processing (ICIP), pp. 977-981, Sept., 2017. [DOI: 10.1109/ICIP. 2017.8296427]
 Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, “Pyramid Scene Parsing Network”, IEEE Confernce on Computer Vision and Pattern Recognition (CVPR), pp. 6230 - 6239, July 2017. [DOI: 10.1109/CVPR.2017.660]
 Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolution Nets, Atrous Convolution, and Fully Connected CRFs”, IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, Vol. 40, Issue 4, pp. 834-848, April, 2018.