An efficient Algorithm for medical image classification using Deep Convolutional Network: Case of Cancer Pathology
Article No.: 77, Pages 1 - 9
Abstract
Automatic classification of medical images especially of tissue images is an important task in computer aided diagnosis (CAD) systems. Deep learning methods such as convolutional networks (ConvNets) outperform other state of-the-art methods in images classification tasks.
This article describes an accurate and efficient algorithms for this challenging problem, and aims to present different convolutional neural networks to classify the tissue images. first, we built a model that consist of feature extraction and the classification with simple CNN, the second model consist of a CNN as feature extractor by removing the classification layers and using the activations of the last fully connected layer to train Random Forest, and the last one using transfer learning --Fine-Tuning-- pre-trained CNN "DenseNet201". Finally, we have evaluated our models using three metrics: accuracy, Precision and F1 Score.
References
[1]
A. Cruzroa, J.C. Caicedo, and F.A. Gonzalez,"Visualpatter n mining in histology image collections using bag of features," Artificial Intelligence in Medicine, vol. 52, no. 2, pp. 91--106, 2011.
[2]
Kumar K, Rao ACS. Breast cancer classification of image using convolutional neural network. In: 2018 4th International Conference on Recent Advances in Information Technology (RAIT). IEEE; 2018. p. 1--6.
[3]
LeCun Y, Bengio Y, Hinton G: Deep learning. Nature. 521(7553): 436--444, 2015.
[4]
Wei L, Xing P, Tang J, Zou Q. PhosPred-RF: a novel sequence-based predictor for phosphorylation sites using sequential information only[J]. IEEE Trans NanoBiosci 2017.
[5]
Wei L, Tang J, Zou Q. Local-DPP: an improved DNA-binding protein prediction method by exploring local evolutionary information[J]. Inf Sci 2017;384:135--44.
[6]
Su R, Zhang C, Pham TD, Davey R, Bischof L, Vallotton P, Sun C. Detection of tubule boundaries based on circular shortest path and polar-transformation of arbitrary shapes[J]. J Microsc 2016;264(2):127--42.
[7]
Po-Whei Huang and Cheng-Hsiung Lee. Automatic classification for pathological prostate images based on fractal analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28:1037--1050, 2009.
[8]
Olcay Sertel, Jun Kong, Umit V. Catalyurek, Gerard Lozanski, Joel H. Saltz, and Metin N. Gurcan. Histopathological image analysis using model-based intermediate representations and color texture: Follicular lymphoma grading. Journal of Signal Processing Systems, 55:169, 2009.
[9]
Le Hou, Dimitris Samaras, Tahsin M. Kurc, Yi Gao, James E. Davis, and Joel H. Saltz. Patch-based convolutional neural network for whole slide tissue image classification. In CVPR, 2016.
[10]
Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, and Andrew H. Beck. Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718, 2016.
[11]
Q. Li, W. Cai, X. Wang, Y. Zhou, D. D. Feng, and M. Chen, "Medical image classification with convolutional neural network,"in Proceedings of 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, pp. 844--848, Singapore, December 2014.
[12]
Byungjae Lee and Kyunghyun Paeng. A robust and effective approach towards accurate metastasis detection and pn-stage classification in breast cancer. arXiv preprint arXiv:1805.12067, 2018.
[13]
Yan Xu, Zhipeng Jia, Liang-Bo Wang, Yuqing Ai, Fang Zhang, Maode Lai, and Eric I-Chao Chang. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation featuresy. BMC Bioinformatics, 18:281, 2017.
[14]
J. Margeta, A. Criminisi, R. C. Lozoya, D. Lee, and N. Ayache, "Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition," Comput Methods Biomech, 2016.
[15]
Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun:A Guide to Convolutional Neural Networks for Computer Vision, Morgan & Claypool publishers 2018.
[16]
V. Nair and G. E. Hinton, "Rectified linear units improve restricted boltzmann machines," in Proceedings of International Conference on Machine Learning, pp. 807--814, Haifa, Israel, June 2010.
[17]
D.MishkinandJ.Matas,"Allyouneedisagoodinit,"http://arxiv.org/abs/1511.06422.
[18]
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 1097--1105.
[19]
Arun, P. and Katiyar, S. (2013). A CNN based Hybrid approach towards automatic age registration. Geodesy and Cartography, 62(1).
[20]
Sebastian Ruder, An overview of gradient descent optimization algorithms, arXiv:1609.04747v2 [cs. LG] 15 Jun 2017.
[21]
L. Breiman, "Random forests," Mach. Learn.vol.45, no.1, app.5--32, 2001.
[22]
V. Lepetit and P. Fua, "Keypoint recognition using randomized trees," IEEE Trans. Pattern Anal. Mach. Intell.2006.
[23]
G. Fanelli, J. Gall, and L. Van Gool, "Real time head pose estimation with random regression forests," IEEE Conf. Comput. Vis. Pattern Recognit.pp.617--624, 2011.
[24]
Jordan M.Malof, Leslie M.Collins, Kyle Bradbul, Richard G.Newell:A Deep Convolutional Neural Network and Random Forest Classifier for Solar Photovoltaic Array Detection in Aerial Imagery, 20.23 nov2016, Burmingham, UK.
[25]
Breiman L (2001) Random forests. Machine learning 45.1: 5--32.
[26]
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700--4708, 2017.
[27]
D. Kingma, and J. L. Ba, (2015). "Adam: a Method for Stochastic Optimization". International Conference on Learning Representations, 1--13.
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Published: 18 May 2020
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NISS2020: The 3rd International Conference on Networking, Information Systems & Security
March 31 - April 2, 2020
Marrakech, Morocco
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