Araştırma Makalesi
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BREAST CANCER DETECTION AND IMAGE EVALUATION USING AUGMENTED DEEP CONVOLUTIONAL NEURAL NETWORKS

Yıl 2018, Cilt: 2 Sayı: 2, 121 - 129, 01.02.2019

Öz

Abstract
Breast malignancy is one of the primary driver of disease demise around the world. Early diagnostics essentially
builds the odds of right treatment and survival, however this procedure is dull and regularly prompts
a contradiction between pathologists. PC supported conclusion frameworks indicated potential for enhancing
the demonstrative precision. In this work, we build up the computational methodology dependent on
increased profound convolution neural systems for bosom malignant growth histology picture characterization.
Hematoxylin and eosin recolored bosom histology microscopy picture dataset is given by Kaggle to
Breast Cancer Histology Images. Our methodology uses a few profound neural system structures and inclination
helped trees classifier. For 5-class grouping assignment, we report 88.4% exactness. For 4-class grouping
undertaking to recognize carcinomas we report 92.3% exactness, 96.2%, and affectability 94.5 by 87.2%
at the high-affectability working point. As far as anyone is concerned, this methodology performs other basic
techniques in computerized histopathological image grouping.

Kaynakça

  • A. N. Tosteson, D. G. Fryback, C. S. Hammond, L. G. Hanna, M. R. Grove, M. Brown, Q. Wang, K. Lindfors, and E. D. Pisano. 2014. “Consequences of false-positive screening mammograms,” JAMA internal medicine, vol. 174, no. 6, (pp. 954–961), 2014.
  • Breast Cancer Wisconsin (Diagnostic) Data Set Predict whether the cancer is benign or malignant https:// www.kaggle.com/uciml/breast-cancer-wisconsin-data
  • D. B. Kopans. 2002. “Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality,” Cancer, vol. 94, no. 2, (pp. 580–1); author reply 581–3, [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/11900247http://www.ncbi.nlm.nih.gov/pubmed/11900247
  • D. B. Kopans. 2015. “An open letter to panels that are deciding guidelines for breast cancer screening,” Breast Cancer Res Treat, vol. 151, no. 1, (pp. 19–25). [Online]. Available: http: //www.ncbi.nlm.nih.gov/pubmed/ 25868866
  • E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov. 2012. “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv preprint arXiv:1207.0580.
  • R. Siegel, K. D. Miller and A. Jemal. 2018. Cancer statistics, 2018, CA: A Cancer Journal for Clinicians, 68 (1), 7–30.
  • H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers. 2016. “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Transactions on Medical Imaging 35, 1285-98.
  • L. Tabar, B. Vitak, H. H. Chen, M. F. Yen, S. W. Duffy, and R. A. Smith. 2001. “Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality,” Cancer, vol. 91, no. 9, (pp. 1724–31).
  • N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and J. Liang. 2016. “Deep Convolutional Neural Networks for Medical Image Analysis: Full Training and Fine Tuning,” IEEE Transactions on Medical Imaging 35, 1299-1312 .
  • S. W. Duffy, L. Tabar, and R. A. Smith. 2002. “The mammographic screening trials: commentary on the recent work by Olsen and Gotzsche,” CA Cancer J Clin, vol. 52, no. 2, (pp. 68–71). [Online]. Available: http:// www.ncbi.nlm.nih.gov/pubmed/11929006http://www.ncbi.nlm.nih.gov/pubmed/11929006
  • S. W. Duffy, L. Tabar, H. H. Chen, M. Holmqvist, M. F. Yen, S. Abdsalah, B. Epstein, E. Frodis, E. Ljungberg, C. Hedborg-Melander, A. Sundbom, M. Tholin, M. Wiege, A. Akerlund, H. M. Wu, T. S. Tung, Y. H. Chiu, C. P. Chiu, C. C. Huang, R. A. Smith, M. Rosen, M. Stenbeck, and L. Holmberg. 2002.
  • “The impact of organized mammography service screening on breast carcinoma mortality in seven swedish counties,” Cancer, vol. 95, no. 3, (pp. 458–69).
  • W. Samuelson and N. Petrick. 2006. “Comparing image detection algorithms using resampling,” 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006. 1312-1315.
  • Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. 1989. “Backpropagation applied to handwritten zip code recognition,” Neural computation, vol. 1, no. 4, (pp. 541–551), 1989.
  • Y. LeCun, Y. Bengio, and G. Hinton. 2015. “Deep learning,” Nature, vol. 521, no. 7553, (pp. 436–444).

ARTIRILMIŞ EVRİŞİMSEL SINIR AĞLARI ILE GÖĞÜS KANSERI TEŞHISI VE GÖRÜNTÜ DEĞERLENDIRMESI

Yıl 2018, Cilt: 2 Sayı: 2, 121 - 129, 01.02.2019

Öz

Özet

Meme kanseri, dünyada insan ölümüne sebep olan başlıca hastalıklardan biridir.Erken teşhis, doğru tedavinin

geliştirilmesini ve sağ kalma olasılığını arttırır, ancak bu süreç belirsizdir ve düzenli olarak patologlar arasında

bir çelişki yaratır. PC destekli sonuç sistemlerinin, görüntükesinliğini arttırmada belirli potansiyele sahip

olduğu belirtilir.Bu çalışmada , kucak malign büyüme histolojisi resim karakterizasyonu için artan derin

evrişim sinir sistemlerine bağlı olan hesaplama metodolojisini geliştiriyoruz. Hematoksilen ve eosin recolored

göğüs histolojisinde mikroskopi resim veri seti Kaggle tarafından Meme Kanseri Histolojisi Görüntülerine

verilmiştir. Metodolojimiz birkaç derin sinir sistemi yapısı kullanır ve meyilli ağaç sınıflandırıcısına yardımcı

olur. 5 sınıflı gruplama ataması için% 88,4 oranında doğruluk bildiririz. Karsinomları tanımayı üstlenen 4 sınıflı

gruplama için yüksek afiniteli çalışma noktasında% 92,3 doğruluk,% 96,2 ve afektabilite% 94,5 oranında

rapor ediyoruz. Herhangi biri söz konusu olduğunda, bu metodoloji bilgisayarlı histopatolojik imge gruplamasında

diğer temel teknikleri de uygular.

Kaynakça

  • A. N. Tosteson, D. G. Fryback, C. S. Hammond, L. G. Hanna, M. R. Grove, M. Brown, Q. Wang, K. Lindfors, and E. D. Pisano. 2014. “Consequences of false-positive screening mammograms,” JAMA internal medicine, vol. 174, no. 6, (pp. 954–961), 2014.
  • Breast Cancer Wisconsin (Diagnostic) Data Set Predict whether the cancer is benign or malignant https:// www.kaggle.com/uciml/breast-cancer-wisconsin-data
  • D. B. Kopans. 2002. “Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality,” Cancer, vol. 94, no. 2, (pp. 580–1); author reply 581–3, [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/11900247http://www.ncbi.nlm.nih.gov/pubmed/11900247
  • D. B. Kopans. 2015. “An open letter to panels that are deciding guidelines for breast cancer screening,” Breast Cancer Res Treat, vol. 151, no. 1, (pp. 19–25). [Online]. Available: http: //www.ncbi.nlm.nih.gov/pubmed/ 25868866
  • E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov. 2012. “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv preprint arXiv:1207.0580.
  • R. Siegel, K. D. Miller and A. Jemal. 2018. Cancer statistics, 2018, CA: A Cancer Journal for Clinicians, 68 (1), 7–30.
  • H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers. 2016. “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Transactions on Medical Imaging 35, 1285-98.
  • L. Tabar, B. Vitak, H. H. Chen, M. F. Yen, S. W. Duffy, and R. A. Smith. 2001. “Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality,” Cancer, vol. 91, no. 9, (pp. 1724–31).
  • N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and J. Liang. 2016. “Deep Convolutional Neural Networks for Medical Image Analysis: Full Training and Fine Tuning,” IEEE Transactions on Medical Imaging 35, 1299-1312 .
  • S. W. Duffy, L. Tabar, and R. A. Smith. 2002. “The mammographic screening trials: commentary on the recent work by Olsen and Gotzsche,” CA Cancer J Clin, vol. 52, no. 2, (pp. 68–71). [Online]. Available: http:// www.ncbi.nlm.nih.gov/pubmed/11929006http://www.ncbi.nlm.nih.gov/pubmed/11929006
  • S. W. Duffy, L. Tabar, H. H. Chen, M. Holmqvist, M. F. Yen, S. Abdsalah, B. Epstein, E. Frodis, E. Ljungberg, C. Hedborg-Melander, A. Sundbom, M. Tholin, M. Wiege, A. Akerlund, H. M. Wu, T. S. Tung, Y. H. Chiu, C. P. Chiu, C. C. Huang, R. A. Smith, M. Rosen, M. Stenbeck, and L. Holmberg. 2002.
  • “The impact of organized mammography service screening on breast carcinoma mortality in seven swedish counties,” Cancer, vol. 95, no. 3, (pp. 458–69).
  • W. Samuelson and N. Petrick. 2006. “Comparing image detection algorithms using resampling,” 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006. 1312-1315.
  • Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. 1989. “Backpropagation applied to handwritten zip code recognition,” Neural computation, vol. 1, no. 4, (pp. 541–551), 1989.
  • Y. LeCun, Y. Bengio, and G. Hinton. 2015. “Deep learning,” Nature, vol. 521, no. 7553, (pp. 436–444).
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makalesi
Yazarlar

Saadaldeen Rashid Ahmed Ahmed Bu kişi benim

Osman Nuri Uçan 0000-0002-4100-0045

Adil Deniz Duru 0000-0003-3014-9626

Oğuz Bayat Bu kişi benim 0000-0001-5988-8882

Yayımlanma Tarihi 1 Şubat 2019
Gönderilme Tarihi 6 Aralık 2018
Kabul Tarihi 3 Şubat 2019
Yayımlandığı Sayı Yıl 2018 Cilt: 2 Sayı: 2

Kaynak Göster

APA Ahmed Ahmed, S. R., Uçan, O. N., Duru, A. D., Bayat, O. (2019). BREAST CANCER DETECTION AND IMAGE EVALUATION USING AUGMENTED DEEP CONVOLUTIONAL NEURAL NETWORKS. AURUM Journal of Engineering Systems and Architecture, 2(2), 121-129.