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Derin öğrenme yöntemleriyle dermoskopi görüntülerinden melanom tespiti: Kapsamlı bir çalışma

Year 2019, Volume: 34 Issue: 4, 2241 - 2260, 25.06.2019
https://doi.org/10.17341/gazimmfd.435217

Abstract

Cilt
kanseri yaygın görülen ve tedavi edilmemesi durumunda ölüme neden olan ciddi
bir hastalıktır. Melanom ise nadir görülmesine rağmen ölüme en çok neden olan
cilt kanseri türüdür. Tüm hastalıklarda olduğu gibi cilt kanserinin erken ve
doğru tespit edilmesi hayati önem taşımaktadır.  Bilgisayar destekli tanı sistemleri cilt
kanseri tespitinde hekimlere ve hastalara yardımcı olabilir. Bilgisayar
destekli tanı sistemlerinde özellikle makine öğrenmesi ve derin öğrenme
algoritmaları etkin bir şekilde kullanılmaktadır. Gerçekleştirilen bu çalışmada
cilt kanseri türü olan melanom için otomatik tanı koyabilecek bir sistem
önerilmektedir. Melanom tanısı için tasarlanan C4Net derin sinir ağ modeli ile
beraber literatürde ön plana çıkmış AlexNet, GoogLeNet, ResNet, VGGNet derin
öğrenme algoritmaları ve Yapay sinir ağları, En yakın komşu algoritması ve
Destek vektör makinesi gibi geleneksel makine öğrenmesi algoritmalarını da
kapsayan detaylı bir deneysel çalışma yapılmıştır. Gerçekleştirilen deneysel
çalışmalarda melanom tanısı için tasarlanan C4Net derin sinir ağ modeli diğer
yöntemlere göre daha yüksek doğrulukta sınıflandırma başarısı göstermiştir. 

References

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  • Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A., & Smith, J. R., Deep learning, sparse coding, and SVM for melanom recognition in dermoscopy images, In International Workshop on Machine Learning in Medical Imaging (pp. 118-126), Springer, Cham, 2015.
  • The International Society for Digital Imaging of the Skin (ISDIS) Archive - https://isic-archive.com/. Erişim tarihi Aralık 5, 2017.
  • Li, Y., Nie, X., & Huang, R., Web spam classification method based on deep belief networks, Expert Systems with Applications, 96, 261-270, 2018.
  • The Skin Cancer Foundation - SkinCancer.org - https://www.skincancer.org/skin-cancer-information/melanoma#panel1-5 - Erişim tarihi Nisan 3, 2018
Year 2019, Volume: 34 Issue: 4, 2241 - 2260, 25.06.2019
https://doi.org/10.17341/gazimmfd.435217

Abstract

References

  • Halk Sağlığı Genel Müdürlüğü - https://hsgm.saglik.gov.tr. Erişim tarihi Nisan 10, 2018
  • Bron, E. E., Smits, M., Van Der Flier, W. M., Vrenken, H., Barkhof, F., Scheltens, P., & Pinto, M., Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. NeuroImage, 111, 562-579, 2015.
  • Oliveira, R. B., Pereira, A. S., & Tavares, J. M. R., Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation, Computer methods and programs in biomedicine, 149, 43-53, 2017.
  • Lottaz, C., Gronwald, W., Spang, R., & Engelmann, J. C., High-Dimensional Profiling for Computational Diagnosis. In Bioinformatics (pp. 205-229), Humana Press, New York, NY, 2017.
  • Demirhan, A., & Güler, İ., Özörgütlemeli Harita Ağlari Ve Gri Düzey Eş Oluşum Matrisleri Ile Görüntü Bölütleme. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 25(2), 2010.
  • Ye, Q. H., Qin, L. X., Forgues, M., He, P., Kim, J. W., Peng, A. C., & Ma, Z. C., Predicting hepatitis B virus–positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning. Nature medicine, 9(4), 416, 2003.
  • Kononenko, I., Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine, 23(1), 89-109, 2001.
  • Berikol, G. B., Yildiz, O., & Özcan, İ. T., Diagnosis of acute coronary syndrome with a support vector machine. Journal of medical systems, 40(4), 84, 2016.
  • Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., & Van Der Laak, J., Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Scientific reports, 6, 26286, 2016
  • Liu, S., Cai, W., Pujol, S., Kikinis, R., & Feng, D., Early diagnosis of Alzheimer's disease with deep learning. In Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on (pp. 1015-1018), IEEE, 2014.
  • Ojala, T., Pietikainen, M., & Maenpaa, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on pattern analysis and machine intelligence, 24(7), 971-987, 2002.
  • Ahonen, T., Hadid, A., & Pietikainen, M., Face description with local binary patterns: Application to face recognition, IEEE transactions on pattern analysis and machine intelligence, 28(12), 2037-2041, 2006.
  • Zhao, G., & Pietikainen, M., Dynamic texture recognition using local binary patterns with an application to facial expressions, IEEE transactions on pattern analysis and machine intelligence, 29(6), 915-928, 2007.
  • Dalal, N., & Triggs, B., Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, CVPR’05-IEEE Computer Society Conference on (Vol. 1, pp. 886-893), 2005
  • Zhu, Q., Yeh, M. C., Cheng, K. T., & Avidan, S., Fast human detection using a cascade of histograms of oriented gradients. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on (Vol. 2, pp. 1491-1498), IEEE, 2006
  • Antonini, M., Barlaud, M., Mathieu, P., & Daubechies, I., Image coding using wavelet transform, IEEE Transactions on image processing, 1(2), 205-220, 1992
  • Chang, T., & Kuo, C. C., Texture analysis and classification with tree-structured wavelet transform, IEEE Transactions on image processing, 2(4), 429-441, 1993.
  • Mehrotra, R., Namuduri, K. R., & Ranganathan, N., Gabor filter-based edge detection. Pattern recognition, 25(12), 1479-1494, 1992.
  • Haralick, R., Shanmugam, K., & Dinstein, I., Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on, SMC-3 (6), 610–621. doi: 10.1109/TSMC.1973.4309314, 1973.
  • Shin, H. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Summers, R. M., Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE transactions on medical imaging, 35(5), 1285-1298, 2016.
  • Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Chen, T., Recent advances in convolutional neural networks. Pattern Recognition, 2017.
  • Chen, X., Xiang, S., Liu, C. L., & Pan, C. H., Vehicle detection in satellite images by hybrid deep convolutional neural networks, IEEE Geoscience and remote sensing letters, 11(10), 1797-1801, 2014.
  • Pal, A., Garain, U., Chandra, A., Chatterjee, R., & Senapati, S., Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network, Computer methods and programs in biomedicine, 159, 59-69, 2018.
  • Chan, T. H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y., PCANet: A simple deep learning baseline for image classification?, IEEE Transactions on Image Processing, 24(12), 5017-5032, 2015
  • Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y., Deep learning-based classification of hyperspectral data, IEEE Journal of Selected topics in applied earth observations and remote sensing, 7(6), 2094-2107, 2014.
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E., Imagenet classification with deep convolutional neural networks, In Advances in neural information processing systems (pp. 1097-1105), 2012.
  • Ciregan, D., Meier, U., & Schmidhuber, J. (2012, June). Multi-column deep neural networks for image classification. In Computer vision and pattern recognition (CVPR), 2012 IEEE conference on (pp. 3642-3649), 2012.
  • Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Sánchez, C. I., A survey on deep learning in medical image analysis, Medical image analysis, 42, 60-88, 2017
  • Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S., Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115, 2017.
  • Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A., & Smith, J. R., Deep learning, sparse coding, and SVM for melanom recognition in dermoscopy images, In International Workshop on Machine Learning in Medical Imaging (pp. 118-126), Springer, Cham, 2015.
  • The International Society for Digital Imaging of the Skin (ISDIS) Archive - https://isic-archive.com/. Erişim tarihi Aralık 5, 2017.
  • Li, Y., Nie, X., & Huang, R., Web spam classification method based on deep belief networks, Expert Systems with Applications, 96, 261-270, 2018.
  • The Skin Cancer Foundation - SkinCancer.org - https://www.skincancer.org/skin-cancer-information/melanoma#panel1-5 - Erişim tarihi Nisan 3, 2018
There are 33 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Oktay Yıldız 0000-0001-9155-7426

Publication Date June 25, 2019
Submission Date June 20, 2018
Acceptance Date March 27, 2019
Published in Issue Year 2019 Volume: 34 Issue: 4

Cite

APA Yıldız, O. (2019). Derin öğrenme yöntemleriyle dermoskopi görüntülerinden melanom tespiti: Kapsamlı bir çalışma. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 34(4), 2241-2260. https://doi.org/10.17341/gazimmfd.435217
AMA Yıldız O. Derin öğrenme yöntemleriyle dermoskopi görüntülerinden melanom tespiti: Kapsamlı bir çalışma. GUMMFD. June 2019;34(4):2241-2260. doi:10.17341/gazimmfd.435217
Chicago Yıldız, Oktay. “Derin öğrenme yöntemleriyle Dermoskopi görüntülerinden Melanom Tespiti: Kapsamlı Bir çalışma”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 34, no. 4 (June 2019): 2241-60. https://doi.org/10.17341/gazimmfd.435217.
EndNote Yıldız O (June 1, 2019) Derin öğrenme yöntemleriyle dermoskopi görüntülerinden melanom tespiti: Kapsamlı bir çalışma. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 34 4 2241–2260.
IEEE O. Yıldız, “Derin öğrenme yöntemleriyle dermoskopi görüntülerinden melanom tespiti: Kapsamlı bir çalışma”, GUMMFD, vol. 34, no. 4, pp. 2241–2260, 2019, doi: 10.17341/gazimmfd.435217.
ISNAD Yıldız, Oktay. “Derin öğrenme yöntemleriyle Dermoskopi görüntülerinden Melanom Tespiti: Kapsamlı Bir çalışma”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 34/4 (June 2019), 2241-2260. https://doi.org/10.17341/gazimmfd.435217.
JAMA Yıldız O. Derin öğrenme yöntemleriyle dermoskopi görüntülerinden melanom tespiti: Kapsamlı bir çalışma. GUMMFD. 2019;34:2241–2260.
MLA Yıldız, Oktay. “Derin öğrenme yöntemleriyle Dermoskopi görüntülerinden Melanom Tespiti: Kapsamlı Bir çalışma”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 34, no. 4, 2019, pp. 2241-60, doi:10.17341/gazimmfd.435217.
Vancouver Yıldız O. Derin öğrenme yöntemleriyle dermoskopi görüntülerinden melanom tespiti: Kapsamlı bir çalışma. GUMMFD. 2019;34(4):2241-60.

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