Derin Öğrenme ile Artırılmış Görüntü Seti üzerinden Cilt Kanseri Tespiti

Öz

Bu çalışmada, son yıllarda görüntü sınıflandırmada artan oranda ilgi gören derin öğrenme ve görüntü işleme yöntemleri kullanılarak kötü huylu (malignant) cilt lezyonlarının erken teşhisini kolaylaştırıcı yapay zekâ tabanlı sınıflandırma deneyleri gerçekleştirilmiştir. Melanom, en kötü huylu ve az görülen bir kanser türü olduğundan dolayı derin öğrenme mimarisini eğitmek için yeterli sayıda eğitim ve test görüntüsü bulmak zordur. Bu nedenle artırılmış veri seti oluşturulmuş ve 6 farklı derin öğrenme mimarisi ile eğitim yapılmıştır. Kötü huylu ve iyi huylu cilt lezyonlarını sınıflandırmak için popüler olan AlexNet, DenseNet-121, ResNet-18, ResNet-34, SqueezeNet ve VGGNet-16 mimarileri kullanılmıştır. Deneyler HAM10000 veri seti üzerinde artırma yapılarak gerçekleştirilmiştir. Deneyler sonucunda en başarılı sonuçları veren Resnet-34 mimarisi ile ortalama %87,5 doğruluk oranı, %94 AUC skoru, %84,5 F-skoru, %87,6 kesinlik değeri elde edilmiştir. Diğer derin öğrenme mimarilerinden elde edilen sonuçlar ve karşılaştırmalı analizler de çalışmada ayrıca sunulmuştur.

Anahtar Kelimeler

• Derin öğrenme
• Evrişimsel sinir ağları
• Transfer öğrenme
• Cilt kanseri
• Sınıflandırma

Skin Cancer Detection via Augmented Image Set with Deep Learning

Öz

In this study, artificial intelligence-based classification experiments were carried out to facilitate the early diagnosis of malignant skin lesions by using deep learning and image processing methods, which have received increasing attention in image classification in recent years. Because melanoma is the most malignant and rarest type of cancer, it is difficult to find enough training and test images to train the deep learning architecture. For this reason, augmented data set was created and training was conducted with 6 different deep learning architectures. Popular architectures AlexNet, DenseNet-121, ResNet-18, ResNet-34, SqueezeNet and VGGNet-16 were used to classify malignant and benign skin lesions. The experiments were carried out on the HAM10000 data set by augmented. As a result of the experiments, with the Resnet-34 architecture, which gave the most successful results, an average of 87.5% accuracy, 94% AUC score, 84.5% F-score, and 87.6% precision were obtained. The results and comparative analyzes obtained from other deep learning architectures are also presented in the study.

Anahtar Kelimeler

• Deep learning
• Convolutional neural network
• Transfer learning
• Skin cancer
• Classification

Kaynakça

1. Abbas Q, Celebi ME, Serrano C, Garcia IF, Ma G. 2013. Pattern classification of dermoscopy images: A perceptually uniform model. Pattern Recog, 46(1): 86-97.
2. Adegun AA, Viriri S. 2020. FCN-based DenseNet framework for automated detection and classification of skin lesions in dermoscopy images. IEEE Access, 8: 150377-150396.
3. Anonymous, 2021. Convolutional Neural Networks. URL: https://www.mathworks.com/discovery/convolutional-neural-network-matlab.html (erişim Tarihi: 10.05.2021)
4. Ayan E, Ünver HM. 2018. Data augmentation importance for classification of skin lesions via deep learning. Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), pp. 1-4, 18-19 April 2018, İstanbul, Turkey.
5. Balaji VR, Suganthi ST, Rajadevi R, Kumar VK, Balaji BS, Pandiyan S. 2020. Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier. Measurement, 163: 107922.
6. Binder M, Schwarz M, Winkler A, Steiner A, Kaider A, Wolff K, Pehamberger H. 1995. Epiluminescence microscopy: a useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Archives of Dermatol, 131(3): 286-291.
7. Brinker TJ, Hekler A, Utikal J S, Grabe N, Schadendorf D, Klode J, Von Kalle C. 2018. Skin cancer classification using convolutional neural networks: systematic review. J Medical Internet Res, 20(10): e11936.
8. Capdehourat G, Corez A, Bazzano A, Alonso R, Musé P. 2011. Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions. Pattern Recog Letters, 32(16): 2187-2196.

9. Celebi ME, Iyatomi H, Stoecker WV, Moss RH, Rabinovitz HS, Argenziano G, Soyer HP. 2008. Automatic detection of blue-white veil and related structures in dermoscopy images. Comput Medical Imaging and Grap, 32(8): 670-677.
10. Celebi ME, Kingravi HA, Uddin B, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH. 2007. A methodological approach to the classification of dermoscopy images. Comput Medical Imaging and Grap, 31(6): 362-373.
11. Cinarer G, Emiroglu BG. 2020. Classification of brain tumours using radiomic features on MRI. New Trends and Issues Proc on Adv in Pure and App Sci, 12: 80–90.
12. Deepak S, Ameer PM. 2019. Brain tumor classification using deep CNN features via transfer learning. Comp in Biol and Medicine, 111: 103345.
13. Deng L, Yu D. 2014. Deep learning: methods and applications. Foundations and Trends in Signal Proc, 7(3–4): 197-387.
14. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639): 115-118.
15. Fırıldak K, Talu MF. 2019. Evrişimsel sinir ağlarında kullanılan transfer öğrenme yaklaşımlarının incelenmesi. Bilgisayar Bil, 4(2): 88-95.
16. Garbe C, Peris K, Hauschild A, Saiag P, Middleton M, Spatz A, Eggermont A. 2010. Diagnosis and treatment of melanoma: European consensus-based interdisciplinary guideline. European J Cancer, 46(2): 270-283.
17. Hameed N, Shabut A M, Ghosh M K, Hossain M A. 2020. Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques. Expert Sys with App, 141: 112961.
18. Harangi B. 2018. Skin lesion classification with ensembles of deep convolutional neural networks. J Biomed Informatics, 86: 25-32.
19. He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 27-30 June 2016, Laas Vegas, USA.
20. Howard J, Gugger S. 2020. Fastai: A layered API for deep learning. Information, 11(2): 108.
21. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. 2017. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 21-26 July 2017, Honolulu, HI, USA.
22. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360.
23. Jerant, AF, Johnson JT, Sheridan CD, Caffrey TJ. 2000. Early detection and treatment of skin cancer. American Family Physician, 62(2): 357-368.
24. Kassani SH, Kassani PH. 2019. A comparative study of deep learning architectures on melanoma detection. Tissue and Cell, 58: 76-83.
25. Kawahara J, BenTaieb A, Hamarneh G. 2016. Deep features to classify skin lesions. In: 2016 IEEE 13th International symposium on biomedical imaging (ISBI), pp. 1397-1400, 13-16 April 2016, Prague, Czech Republic.
26. Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC. 2019. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recog Letters, 125: 1-6.
27. Kittler H, Pehamberger H, Wolf K, Binder M. 2002. Diagnostic accuracy of dermoscopy. The Lancet Oncol, 3(3): 159-165.
28. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Inf Proc Sys, 25: 1097-1105.
29. LeCun Y, Bottou L, Bengio Y, Haffner P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11): 2278-2324.
30. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI,. 2017. A survey on deep learning in medical image analysis. Medical Image Anal: 42, 60-88.
31. Lopez AR, Giro-i-Nieto X, Burdick J, Marques O. 2017. Skin lesion classification from dermoscopic images using deep learning techniques. In: 2017 13th IASTED International conference on biomedical engineering (BioMed), IEEE, pp. 49-54, February 20 – 21, 2017. Innsbruck, Austria.
32. Narayanamurthy V, Padmapriya P, Noorasafrin A, Pooja B, Hema K, Nithyakalyani K, Samsuri F. 2018. Skin cancer detection using non-invasive techniques. RSC Adv, 8(49): 28095-28130.
33. Oliveira RB, Papa JP, Pereira AS, Tavares JMR. 2018. Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput and App, 29(3): 613-636.
34. Pacal I, Karaboga D, Basturk A, Akay B, Nalbantoglu U. 2020. A comprehensive review of deep learning in colon cancer. Comp in Biol and Medicine, 126: 104003.
35. Premaladha, J, Ravichandran KS. 2016. Novel approaches for diagnosing melanoma skin lesions through supervised and deep learning algorithms. J Medical Sys, 40(4): 1-12.
36. Psaty EL, Halpern AC. 2009. Current and emerging technologies in melanoma diagnosis: the state of the art. Clinics in Dermatol, 27(1): 35-45.
37. Purnama IKE, Hernanda AK, Ratna AAP, Nurtanio I, Hidayati AN, Purnomo MH, Rachmadi RF. 2019. Disease classification based on dermoscopic skin images using convolutional neural network in teledermatology system. In: 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), pp. 1-5, November 19 - 20, 2019, Surabaya, Cava.
38. Quang NH. 2017. Automatic skin lesion analysis towards melanoma detection. In: 2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES), pp. 106-111, 15-17 November 2017, Hanoi, Vietnam.
39. Rashid H, Tanveer MA, Khan HA. 2019. Skin lesion classification using GAN based data augmentation. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 916-919, 23-27 July 2019, Berlin, Germany.
40. Rey-Barroso L, Peña-Gutiérrez S, Yáñez C, Burgos-Fernández FJ, Vilaseca M, Royo S. 2021. Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review. Sensors, 21(1): 252.
41. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Fei-Fei L. 2015. Imagenet large scale visual recognition challenge. Int J Comp Vision, 115(3): 211-252.
42. Schmidhuber J. 2015. Deep learning in neural networks: An overview. Neural Networks, 61: 85-117.
43. Siegel RL, Miller KD, Fuchs HE, Jemal A. 2021. Cancer Statistics, 2021. CA: a Cancer J Clinicians, 71(1): 7-33.
44. Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
45. Thomas L, Puig S. 2017. Dermoscopy, digital dermoscopy and other diagnostic tools in the early detection of melanoma and follow-up of high-risk skin cancer patients. Acta Dermato Venereologica, 218: 14-21.
46. Tschandl P, Rosendahl C, Kittler H. 2018. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data, 5(1): 1-9.
47. Xie Y, Zhang J, Xia Y, Shen C. 2020. A mutual bootstrapping model for automated skin lesion segmentation and classification. IEEE Transact on Medical Imag, 39(7): 2482-2493.