Classification of Different Cancer Types by Deep Convolutional Neural Networks
Öz
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.
Anahtar Kelimeler
Kaynakça
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- [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
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- [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,
Ayrıntılar
Birincil Dil
İngilizce
Konular
-
Bölüm
Araştırma Makalesi
Yazarlar
Yayımlanma Tarihi
1 Nisan 2018
Gönderilme Tarihi
13 Eylül 2017
Kabul Tarihi
8 Ocak 2018
Yayımlandığı Sayı
Yıl 2018 Cilt: 6
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