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Ahmadi Mehr, Reza, Ameri, Ali. (1401). Skin Cancer Detection Based on Deep Learning. سامانه مدیریت نشریات علمی, 12(6), 559-568. doi: 10.31661/jbpe.v0i0.2207-1517
Reza Ahmadi Mehr; Ali Ameri. "Skin Cancer Detection Based on Deep Learning". سامانه مدیریت نشریات علمی, 12, 6, 1401, 559-568. doi: 10.31661/jbpe.v0i0.2207-1517
Ahmadi Mehr, Reza, Ameri, Ali. (1401). 'Skin Cancer Detection Based on Deep Learning', سامانه مدیریت نشریات علمی, 12(6), pp. 559-568. doi: 10.31661/jbpe.v0i0.2207-1517
Ahmadi Mehr, Reza, Ameri, Ali. Skin Cancer Detection Based on Deep Learning. سامانه مدیریت نشریات علمی, 1401; 12(6): 559-568. doi: 10.31661/jbpe.v0i0.2207-1517

Skin Cancer Detection Based on Deep Learning

Journal of Biomedical Physics and Engineering
دوره 12، شماره 6، اسفند 2022، صفحه 559-568    اصل مقاله (1.61 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2207-1517
نویسندگان
Reza Ahmadi Mehr1؛ Ali Ameri* 2
1MSc, Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2PhD, Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
Background: The conventional procedure of skin-related disease detection is a visual inspection by a dermatologist or a primary care clinician, using a dermatoscope. The suspected patients with early signs of skin cancer are referred for biopsy and histopathological examination to ensure the correct diagnosis and the best treatment. Recent advancements in deep convolutional neural networks (CNNs) have achieved excellent performance in automated skin cancer classification with accuracy similar to that of dermatologists. However, such improvements are yet to bring about a clinically trusted and popular system for skin cancer detection. 
Objective: This study aimed to propose viable deep learning (DL) based method for the detection of skin cancer in lesion images, to help physicians in diagnosis.
Material and Methods: In this analytical study, a novel DL based model was proposed, in which other than the lesion image, the patient’s data, including the anatomical site of the lesion, age, and gender were used as the model input to predict the type of the lesion. An Inception-ResNet-v2 CNN pretrained for object recognition was employed in the proposed model. 
Results: Based on the results, the proposed method achieved promising performance for various skin conditions, and also using the patient’s metadata in addition to the lesion image for classification improved the classification accuracy by at least 5% in all cases investigated. On a dataset of 57536 dermoscopic images, the proposed approach achieved an accuracy of 89.3%±1.1% in the discrimination of 4 major skin conditions and 94.5%±0.9% in the classification of benign vs. malignant lesions.  
Conclusion: The promising results highlight the efficacy of the proposed approach and indicate that the inclusion of the patient’s metadata with the lesion image can enhance the skin cancer detection performance. 
کلیدواژه‌ها
CNN؛ Deep learning؛ Dermoscopy؛ Lesion؛ Metadata؛ Skin Cancer
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مراجع
  1. American Cancer Society. Cancer Facts & Figures. 2021. [accessed 2021 September 3]. Available from: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2021.html.
  2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209-49. doi: 10.3322/caac.21660. PubMed PMID: 33538338.
  3. Stern RS. Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch Dermatol. 2010;146(3):279-82. doi: 10.1001/archdermatol.2010.4. PubMed PMID: 20231498.
  4. Miller AJ, Mihm MC Jr. Melanoma. N Engl J Med. 2006;355(1):51-65. doi: 10.1056/NEJMra052166. PubMed PMID: 16822996.
  5. Madan V, Lear JT, Szeimies RM. Non-melanoma skin cancer. 2010;375(9715):673-85. doi: 10.1016/S0140-6736(09)61196-X. PubMed PMID: 20171403.
  6. Rudolph C, Schnoor M, Eisemann N, Katalinic A. Incidence trends of nonmelanoma skin cancer in Germany from 1998 to 2010. J Dtsch Dermatol Ges. 2015;13(8):788-97. doi: 10.1111/ddg.12690. PubMed PMID: 26213814.
  7. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8. doi: 10.1038/nature21056. PubMed PMID: 28117445. PubMed PMCID: PMC8382232.
  8. Kittler H, Pehamberger H, Wolff K, Binder M. Diagnostic accuracy of dermoscopy. Lancet Oncol. 2002;3(3):159-65. doi: 10.1016/s1470-2045(02)00679-4. PubMed PMID: 11902502.
  9. Codella NC, Nguyen QB, Pankanti S, Gutman DA, Helba B, Halpern AC, Smith JR. Deep learning ensembles for melanoma recognition in dermoscopy images. IBM Journal of Research and Development. 2017;61(4/5):5-1. doi: 10.1147/JRD.2017.2708299.
  10. Ramlakhan K, Shang Y. A mobile automated skin lesion classification system. In 23rd International Conference on Tools with Artificial Intelligence; Boca Raton, FL, USA: IEEE; 2011. p. 138-41.
  11. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25. NeurIPS Proceedings; 2012.
  12. Silvestre Salvador JF, Romero-Pérez D, Encabo-Durán B. Atopic Dermatitis in Adults: A Diagnostic Challenge. J Investig Allergol Clin Immunol. 2017;27(2):78-88. doi: 10.18176/jiaci.0138. PubMed PMID: 28071589.
  13. The Skin Cancer Foundation. Skin Cancer Facts & Statistics. 2021. [accessed 2021 September 4]. Available from: https://www.skincancer.org/skin-cancer-information/skin-cancer-facts/.
  14. Combalia M, Codella NC, Rotemberg V, Helba B, Vilaplana V, Reiter O, et al. Bcn20000: Dermoscopic lesions in the wild. 2019. doi: 10.48550/arXiv:1908.02288.
  15. Codella NCF, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, et al. Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). International Symposium on Biomedical Imaging; Washington, DC, USA: IEEE; 2018.
  16. Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data. 2018;5:180161. doi: 10.1038/sdata.2018.161. PubMed PMID: 30106392. PubMed PMCID: PMC6091241.
  17. International Skin Imaging Collaboration. The ISIC 2020 Challenge Dataset. ISIC; 2020. Available from: https://challenge2020.isic-archive.com/.
  18. Rotemberg V, Kurtansky N, Betz-Stablein B, Caffery L, Chousakos E, Codella N, et al. A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Sci Data. 2021;8(1):34. doi: 10.1038/s41597-021-00815-z. PubMed PMID: 33510154. PubMed PMCID: PMC7843971.
  19. Pacheco AGC, Lima GR, Salomão AS, Krohling B, Biral IP, De Angelo GG, et al. PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones. Data Brief. 2020;32:106221. doi: 10.1016/j.dib.2020.106221. PubMed PMID: 32939378. PubMed PMCID: PMC7479321.
  20. Pacheco AGC, Krohling RA. The impact of patient clinical information on automated skin cancer detection. Comput Biol Med. 2020;116:103545. doi: 10.1016/j.compbiomed.2019.103545. PubMed PMID: 31760271.
  21. Groh M, Harris C, Soenksen L, Lau F, Han R, Kim A, et al. Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); IEEE; 2021. p. 1820-8.
  22. Szegedy C, Ioffe S, Vanhoucke V, Alemi AA. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. In Thirty-first AAAI conference on artificial intelligence; California: AAAI Press; 2017.
  23. Lantz B. Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications. UK: Packt Publishing Ltd; 2013.
  24. Buckman J, Roy A, Raffel C, Goodfellow I. Thermometer encoding: One hot way to resist adversarial examples. International Conference on Learning Representations; 2018.
  25. Dorj UO, Lee KK, Choi JY, Lee M. The skin cancer classification using deep convolutional neural network. Multimedia Tools and Applications. 2018;77:9909–24. doi: 10.1007/s11042-018-5714-1.
  26. Hekler A, Utikal JS, Enk AH, Hauschild A, Weichenthal M, Maron RC, et al. Superior skin cancer classification by the combination of human and artificial intelligence. Eur J Cancer. 2019;120:114-21. doi: 10.1016/j.ejca.2019.07.019. PubMed PMID: 31518967.
  27. Jinnai S, Yamazaki N, Hirano Y, Sugawara Y, Ohe Y, Hamamoto R. The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning. Biomolecules. 2020;10(8):1123. doi: 10.3390/biom10081123. PubMed PMID: 32751349. PubMed PMCID: PMC7465007.
  28. Combalia M, Codella N, Rotemberg V, Carrera C, Dusza S, Gutman D, et al. Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge. Lancet Digit Health. 2022;4(5):e330-9. doi: 10.1016/S2589-7500(22)00021-8. PubMed PMID: 35461690. PubMed PMCID: PMC9295694.
  29. Ali K, Shaikh ZA, Khan AA, Laghari AA. Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer. Neuroscience Informatics. 2022;2(4):100034. doi: 10.1016/j.neuri.2021.100034.
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