Skip to main content
SpringerLink
Log in

Skin lesion classification on dermatoscopic images using effective data augmentation and pre-trained deep learning approach

  • Track 2: Medical Applications of Multimedia
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Skin cancer is a severe disease that is common and causes death if left untreated. When skin cancer is detected early through dermatoscopic imaging, the possibility of definitive treatment is very high. Although melanoma is one of the fatal types of skin cancer, early detection dramatically increases the chances of survival. There is a low morbidity rate and limited actual data to study this deadly disease. This is a significant handicap in the application of machine learning techniques. Accurate diagnosis is essential because of the similarity of some types of lesions. The accuracy of the diagnosis is related to the professional experience of the specialist. The development of rapid and successful computerized diagnostic systems for the diagnosis and classification of skin cancer has become increasingly important. Deep learning-based applications are especially new trend in the detection of diseases from medical images. In this study, an effective data augmentation and a pre-trained deep learning approach are proposed for skin lesion classification. A hybrid network model called the Inception-Resnet-v2 is proposed to classify skin cancer images. The main aim of this study is to increase the number of images in the dataset by applying the affine transformation technique (data augmentation) and analyzing its effect on the skin cancer classification system. The highest reported accuracy in this study with an augmented dataset is 95.09% for the Inception-Resnet-v2 model while the same model achieved 83.59% with the original dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Abbas Q, Celebi ME, Serrano C, Garcia IF, Ma G (2013) Pattern classification of dermoscopy images: a perceptually uniform model. Pattern Recognit 46(1):86–97. https://doi.org/10.1016/j.patcog.2012.07.027

    Article  Google Scholar 

  2. Akram T, Lodhi HMJ, Naqvi SR, Naeem S, Alhaisoni M, Ali M, Qadri NN (2020) A multilevel features selection framework for skin lesion classification. HCIS 10(1):1–26. https://doi.org/10.1186/s13673-020-00216-y

    Article  Google Scholar 

  3. Aldwgeri A, Abubacker NF (2019) Ensemble of deep convolutional neural network for skin lesion classification in dermoscopy images. In: International visual informatics conference. Springer, Cham, pp 214–226. https://doi.org/10.1007/978-3-030-34032-2_20

  4. Ali MS, Miah MS, Haque J, Rahman M, Islam MK (2021) An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models. Mach Learn Appl 5:100036. https://doi.org/10.1016/j.mlwa.2021.100036

    Article  Google Scholar 

  5. Alqudah AM, Alquraan H, Qasmieh IA (2019) Segmented and non-segmented skin lesions classification using transfer learning and adaptive moment learning rate technique using pretrained convolutional neural network. In: Journal of biomimetics, biomaterials and biomedical engineering, vol 42. Trans Tech Publications Ltd, pp 67–78

  6. Brinker TJ, Hekler A, Enk AH, Berking C, Haferkamp S, Hauschild A, Utikal JS (2019) Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer 119:11–17. https://doi.org/10.1016/j.ejca.2019.05.023

    Article  Google Scholar 

  7. 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 Recogn Lett 32(16):2187–2196. https://doi.org/10.1016/j.patrec.2011.06.015

    Article  Google Scholar 

  8. 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 Med Imaging Graph 32 (8):670–677. https://doi.org/10.1016/j.compmedimag.2008.08.003

    Article  Google Scholar 

  9. Cengil E, Çınar A, Yıldırım M (2021) Hybrid convolutional neural network architectures for skin cancer classification. Avrupa Bilim ve Teknoloji Dergisi, Ejosat Special Issue 2021 (ICAENS), pp 694–701. https://doi.org/10.31590/ejosat.1010266

  10. Chaturvedi S, Tembhurne JV, Diwan T (2020) A multi-class skin Cancer classification using deep convolutional neural networks. Multimed Tools Appl 79(39):28477–28498. https://doi.org/10.1007/s11042-020-09388-2

    Article  Google Scholar 

  11. Demir F (2021) Derin Öğrenme Tabanlı Yaklaşıımla Kötü Huylu Deri Kanserinin Dermatoskopik Görüntülerden Saptanması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 33(2):617–624. https://doi.org/10.35234/fumbd.900170

    Article  MathSciNet  Google Scholar 

  12. Ergün E, Kılıç K (2021) Derin Öğrenme ile Artırılmış Görüntü Seti üzerinden Cilt Kanseri Tespiti. Black Sea J Eng Sci 4(4):192–200. https://doi.org/10.34248/bsengineering.938520

    Article  Google Scholar 

  13. 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. https://doi.org/10.1038/nature21056

    Article  Google Scholar 

  14. Fabbrocini G, Triassi M, Mauriello MC, Torre G, Annunziata MC, De Vita V, Monfrecola G (2010) Epidemiology of skin cancer: role of some environmental factors. Cancers 2(4):1980–1989. https://doi.org/10.3390/cancers2041980

    Article  Google Scholar 

  15. Fabbrocini G, De Vita V, Pastore F, D’Arco V, Mazzella C, Annunziata MC, Monfrecola A (2011) Teledermatology: from prevention to diagnosis of nonmelanoma and melanoma skin cancer. Int J Telemedicine Appl 2011. https://doi.org/10.1155/2011/125762

  16. Fattahi M, Moattar MH, Forghani Y (2022) Improved cost-sensitive representation of data for solving the imbalanced big data classification problem. Journal of Big Data 9(1):1–24. https://doi.org/10.1186/s40537-022-00617-z

    Article  Google Scholar 

  17. Ferreira CA et al (2018) Classification of breast cancer histology images through transfer learning using a pre-trained inception Resnet V2. In: Campilho A, Karray F, ter Haar Romeny B (eds) Image analysis and recognition. ICIAR 2018. Lecture notes in computer science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_86

  18. Garg R, Maheshwari S, Shukla A (2021) Decision support system for detection and classification of skin cancer using CNN. In: Innovations in computational intelligence and computer vision. Springer, Singapore, pp 578–586. https://doi.org/10.1007/978-981-15-6067-5_65

  19. Goceri E (2020) Image augmentation for deep learning based lesion classification from skin images. In: 2020 IEEE 4th international conference on image processing, applications and systems (IPAS). IEEE, pp 144–148. https://doi.org/10.1109/IPAS50080.2020.9334937

  20. Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, Zalaudek I (2018) Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 29(8):1836–1842. https://doi.org/10.1093/annonc/mdy166

    Article  Google Scholar 

  21. Hameed A, Umer M, Hafeez U et al (2021) Skin lesion classification in dermoscopic images using stacked convolutional neural network. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-021-03485-2

  22. 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

  23. Hoang L, Lee SH, Lee EJ, Kwon KR (2022) Multiclass skin lesion classification using a novel lightweight deep learning framework for smart healthcare. Appl Sci 12(5):2677. https://doi.org/10.3390/app12052677

    Article  Google Scholar 

  24. Hosny KM, Kassem MA, Foaud MM (2019) Classification of skin lesions using transfer learning and augmentation with alex-net. PloS one 14(5):e0217293. https://doi.org/10.1371/journal.pone.0217293

    Article  Google Scholar 

  25. Kalaivani A, Karpagavalli S, Bibi MJ (2021) A deep learning approach for real-time defect classification in skin disease. New Arch-International Journal of Contemporary Architecture 8(2):443–451

    Google Scholar 

  26. Kassani SH, Kassani PH (2019) A comparative study of deep learning architectures on melanoma detection. Tissue Cell 58:76–83. https://doi.org/10.1016/j.tice.2019.04.009

    Article  Google Scholar 

  27. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25

  28. Li Y, Wang K (2020) Modified convolutional neural network with global average pooling for intelligent fault diagnosis of industrial gearbox. Eksploatacja i Niezawodność 22(1). https://doi.org/10.17531/ein.2020.1.8

  29. Mehra A, Bhati A, Kumar A, Malhotra R (2021) Skin cancer classification through transfer learning using ResNet-50. In: Emerging technologies in data mining and information security. Springer, Singapore, pp 55–62. https://doi.org/10.1007/978-981-33-4367-2_6

  30. Moataz L, Salama GI, Abd Elazeem MH (2021) Skin cancer diseases classification using deep convolutional neural network with transfer learning model. In: Journal of physics: conference series, vol 2128, no 1. IOP Publishing, p 012013

  31. Mohbey K (2020) Multi-class approach for user behavior prediction using deep learning framework on twitter election dataset. J Data Inf Manag 2(1):1–14. https://doi.org/10.1007/s42488-019-00013-y

    Article  Google Scholar 

  32. Nami N, Giannini E, Burroni M, Fimiani M, Rubegni P (2012) Teledermatology: state-of-the-art and future perspectives. Expert Rev Dermatol 7(1):1–3. https://doi.org/10.1586/edm.11.79

    Article  Google Scholar 

  33. 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. https://doi.org/10.1039/C8RA04164D

    Article  Google Scholar 

  34. Nugroho AA, Slamet I, Sugiyanto (2019) Skins cancer identification system of HAMl0000 skin cancer dataset using convolutional neural network. In: AIP conference proceedings, vol 2202, no 1. AIP Publishing LLC, p 020039. https://doi.org/10.1063/1.5141652

  35. Pai K, Giridharan A (2019) Convolutional neural networks for classifying skin lesions. In: TENCON 2019-2019 IEEE region 10 conference (TENCON). IEEE, pp 1794–1796. https://doi.org/10.1109/TENCON.2019.8929461

  36. Pouyanfar S, Chen SC, Shyu ML (2017) An efficient deep residual-inception network for multimedia classification. In: 2017 IEEE international conference on multimedia and expo (ICME). IEEE, pp 373–378, DOI https://doi.org/10.1109/ICME.2017.8019447, (to appear in print)

  37. Purnama IKE et al (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. https://doi.org/10.1109/CENIM48368.2019.8973303

  38. Ramachandro M, Daniya T, Saritha B (2021) Skin cancer detection using machine learning algorithms. In: 2021 innovations in power and advanced computing technologies (i-PACT). IEEE, pp 1–7. https://doi.org/10.1109/i-PACT52855.2021.9696874

  39. Ratul MAR, Mozaffari MH, Lee WS, Parimbelli E (2020) Skin lesions classification using deep learning based on dilated convolution. BioRxiv, p 860700. https://doi.org/10.1101/860700

  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. https://doi.org/10.3390/s21010252

    Article  Google Scholar 

  41. Roccetti M, Delnevo G, Casini L, Mirri S (2021) An alternative approach to dimension reduction for pareto distributed data: a case study. Journal of Big Data 8(1):1–23. https://doi.org/10.1186/s40537-021-00428-8

    Article  Google Scholar 

  42. Salamaa WM, Aly MH (2021) Deep learning design for benign and malignant classification of skin lesions: a new approach. Multimed Tools Appl 80:26795–26811. https://doi.org/10.1007/s11042-021-11000-0

    Article  Google Scholar 

  43. Salma W, Eltrass AS (2022) Automated deep learning approach for classification of malignant melanoma and benign skin lesions. Multimed Tools Appl. https://doi.org/10.1007/s11042-022-13081-x

  44. Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA: a cancer journal for clinicians. https://doi.org/10.3322/caac.21708

  45. Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ (2021) Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors 21(8):2852. https://doi.org/10.3390/s21082852

    Article  Google Scholar 

  46. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  47. 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 Derm Venereol 97. https://doi.org/10.2340/00015555-2719

  48. Tschandl P, Rosendahl C, Kittler H (2018) The HAM 10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5:180161. https://doi.org/10.1038/sdata.2018.161

    Article  Google Scholar 

  49. Ural A, Kilimci Z.H (2021) The prediction of chiral metamaterial resonance using convolutional neural networks and conventional machine learning algorithms. Int J Comput Exp Sci Eng 7(3):156–163. https://doi.org/10.22399/ijcesen.973726

    Article  Google Scholar 

  50. Verma R, Kumar N, Patil A, Kurian NC, Rane S, Graham S, Sethi A (2021) MoNuSAC2020: a multi-organ nuclei segmentation and classification challenge. IEEE Trans Med Imaging 40(12):3413–3423. https://doi.org/10.1109/TMI.2021.3085712

    Article  Google Scholar 

  51. Wang J, He X, Faming S, Lu G, Cong H, Jiang Q (2021) A real-time bridge crack detection method based on an improved inception-resnet-v2 structure. IEEE Access 9:93209–93223. https://doi.org/10.1109/ACCESS.2021.3093210

    Article  Google Scholar 

  52. Xie Y, Zhang J, Xia Y, Shen C (2020) A mutual bootstrapping model for automated skin lesion segmentation and classification. IEEE Trans Med Imaging 39 (7):2482–2493. https://doi.org/10.1109/TMI.2020.2972964

    Article  Google Scholar 

  53. Zhang Y, Davison BD (2019) Modified distribution alignment for domain adaptation with pre-trained inception ResNet. https://doi.org/10.48550. arXiv:1904.02322

  54. Zunair H, Hamza AB (2020) Melanoma detection using adversarial training and deep transfer learning. Phys Med Biol 65(13):135005. https://doi.org/10.1088/1361-6560/ab86d3

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Ataturk University, 25240, Erzurum, Turkey

    Ferhat Bozkurt

Authors
  1. Ferhat Bozkurt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ferhat Bozkurt.

Ethics declarations

Conflict of Interests

The author has declared that no conflict of interests or competing interests exist.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bozkurt, F. Skin lesion classification on dermatoscopic images using effective data augmentation and pre-trained deep learning approach. Multimed Tools Appl 82, 18985–19003 (2023). https://doi.org/10.1007/s11042-022-14095-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-14095-1

Keywords

Search

Navigation