Classification of Different Cancer Types by Deep Convolutional Neural Networks

Year 2018, Volume: 6 , 56 - 59, 01.04.2018

H. Selcuk Nogay

https://doi.org/10.17694/bajece.410250

Cited By: 4

Abstract

In this study, ten
different types of cancer were classified with deep convolutional neural
networks (DCNN). A total of 10,000 MRI (Magnetic Resonance Imaging) data were
used for ten cancer patients, including 1000 MRI data for each cancer type.
Although the images were reduced to 28x28 pixels, the DCNN model performed
classification with an accuracy rate of 0.98 after 27 seconds and 15 epochs of
training. The error rate in the last epoch in the study is also very close to
zero. A highly successful classification has been achieved with the proposed
DCNN model.

Keywords

Deep Convolutional Neural Network, Cancer Types, Classification, Accuracy

References

• [1]. U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Hojjat Adeli, Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals, Computers in Biology and Medicine, (2017) 1–9
• [2]. M.E. Paoletti, J.M. Haut, J. Plaza, A. Plaza, A new deep convolutional neural network for fast hyperspectral image classification, ISPRS Journal of Photogrammetry and Remote Sensing , 31, May, 2017, 1-28
• [3]. Pegah Khosravi, Ehsan Kazemi, Marcin Imielinski, Olivier Elemento, Iman Hajirasouliha, Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images, EbioMedicine, 2017, 1-12
• [4]. Y. Zheng, Zhiguo Jiang, F. Xie, H. Zhang , Y. Ma , H. Shi , Yu Zhao Feature extraction from histopathological images based on nucleus-guided convolutional neural network for breast lesion classification, Pattern Recognition 71 (2017) 14–25,
• [5]. Vallières, M. et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer, Sci Rep, 7, 10117 (2017). doi: 10.1038/s41598-017-10371-5
• [6]. http://www.cancerimagingarchive.net/ , date of access: 10 Jan 2018,
• [7]. M. Dais Ferreira, Débora Cristina Cor rêa, Luis Gustavo Nonato, Rodrigo Fernandes de Mello, Designing architectures of convolutional neural networks to solve practical problems, Expert Systems With Applications 94 (2018) 205–217
• [8]. B. Krismono Triwijoyo, Widodo Budiharto, Edi Abdurachman, The Classification of Hypertensive Retinopathy using Convolutional Neural Network 2nd International Conference on Computer Science and Computational Intelligence 2017, ICCSCI 2017, 13-14 October 2017, Bali, Indonesia,
• [9]. U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam, Arkadiusz Gertych, Ru San Tan, A deep convolutional neural network model to classify heartbeats, Computers in Biology and Medicine 89 (2017) 389–396
• [10]. U.K. Lopes , J.F. Valiati, Pre-trained convolutional neural networks as feature extractors for tuberculosis detection, Computers in Biology and Medicine 89 (2017) 135–143.
• [11]. Mads Dyrmann, Henrik Karstoft, Henrik Skov Midtiby, Plant species classification using deep convolutional neural network, biosystems engineering, 151, (2016), 72 – 80
• [12]. Saddam Hussain , Syed Muhammad Anwar , Muhammad Majid, Segmentation of glioma tumors in brain using deep convolutional neural network, Neurocomputing, (2017) 1–14
• [13]. Harshita Sharma, Norman Zerbe, Iris Klempert, Olaf Hellwich, Peter Hufnagl, Deep convolutional neural networks for automatic classification ofgastric carcinoma using whole slide.
• [14]. Shiqi Yu, SenJia, ChunyanXu, Convolutional neural networks for hyperspectral image classification, Neurocomputing, 219, (2017), 88–98.
• [15]. Goodfellow, I. J., Warde-farley, D., ve Courville. AMaxout Networks. Proceedings of the 30th International Conference on Machine Learning, Atlanta, Georgia, USA. JMLR: W&CP.(2013). s. 28.