Skip to main content
Book cover

Proceedings of International Joint Conference on Advances in Computational Intelligence pp 55–66Cite as

Skin Lesion Classification Using Convolutional Neural Network for Melanoma Recognition

Part of the Algorithms for Intelligent Systems book series (AIS)

Abstract

Skin cancer, also known as melanoma, is generally diagnosed visually from the dermoscopic images, which is a tedious and time-consuming task for the dermatologist. Such a visual assessment, via the naked eye for skin cancers, is challenging and arduous due to different artifacts such as low contrast, various noise, presence of hair, fiber, and air bubbles. This article proposes a robust and automatic framework for the skin lesion classification (SLC), where we have integrated image augmentation, deep convolutional neural network (DCNN), and transfer learning. The proposed framework was trained and tested on publicly available IEEE International Symposium on Biomedical Imaging (ISBI)-2017 dataset. The obtained average areas under the receiver operating characteristic curve (AUC), recall, precision, and F1-score are, respectively, 0.87, 0.73, 0.76, and 0.74 for the SLC. Our experimental studies for lesion classification demonstrate that the proposed approach can successfully distinguish skin cancer with a high degree of accuracy, which has the capability of skin lesion identification for melanoma recognition.

Keywords

  • Skin cancer
  • Image augmentation
  • Deep convolutional neural network
  • Transfer learning
  • Melanoma recognition

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-16-0586-4_5
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-981-16-0586-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Hardcover Book
USD   279.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Narayanamurthy V, Padmapriya P, Noorasafrin A, Pooja B, Hema K, Nithyakalyani K, Samsuri F et al (2018) Skin cancer detection using non-invasive techniques. RSC Adv 8(49):28095–28130

    CrossRef  Google Scholar 

  2. Ries LA, Harkins D, Krapcho M, Mariotto A, Miller B, Feuer EJ, Clegg LX, Eisner M, Horner MJ, Howlader N et al (2006) SEER cancer statistics review 1975–2003

    Google Scholar 

  3. Zhang N, Cai YX, Wang YY, Tian YT, Wang XL, Badami B (2020) Skin cancer diagnosis based on optimized convolutional neural network. Artif Intell Med 102:101756

    CrossRef  Google Scholar 

  4. Siegel RL, Miller KD, Jemal A (2020) Cancer statistics, 2020. CA: Cancer J Clin 70(1), 7–30

    Google Scholar 

  5. World Health Ranking. https://www.worldlifeexpectancy.com/bangladesh-skin-cancers. Last accessed 1 May 2020

  6. Ge Z, Demyanov S, Chakravorty R, Bowling A, Garnavi R (2017) Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. In: International conference on medical image computing and computer-assisted intervention. Springer, Quebec City, pp 250–258

    Google Scholar 

  7. Smith L, MacNeil S (2011) State of the art in non-invasive imaging of Cutaneous melanoma. Skin Res Technol 17(3):257–269

    CrossRef  Google Scholar 

  8. Hasan MK, Dahal L, Samarakoon PN, Tushar FI, Martí R (2020) DSNet: automatic dermoscopic skin lesion segmentation. Comput Biol Med 120:103738

    CrossRef  Google Scholar 

  9. Jalalian A, Mashohor S, Mahmud R, Karasfi B, Saripan MIB, Ramli ARB (2017) Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection. EXCLI J 16:113–137

    Google Scholar 

  10. Mishraa NK, Celebi ME (2016) An overview of melanoma detection in dermoscopy images using image processing and machine learning. arXiv:1601.07843

  11. Codella NF, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H (2018) Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISIB), hosted by the International skin imaging collaboration (ISIC). In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE, Washington, DC, pp 168–172

    Google Scholar 

  12. Brinker TJ, Hekler A, Utikal JS, Grabe N, Schadendorf D, Klode J, Berking C, Steeb T, Enk AH, von Kalle C (2018) Skin cancer classification using convolutional neural networks: systematic review. J Med Internet Res 20(10):e11936

    CrossRef  Google Scholar 

  13. Ma Z, Tavares JMR et al (2015) A review of the quantification and classification of pigmented skin lesions: from dedicated to hand-held devices. J Med Syst 39(11):177

    CrossRef  Google Scholar 

  14. Menzies SW, Bischof L, Talbot H, Gutenev A, Avramidis M, Wong L, Lo SK, Mackellar G, Skladnev V, McCarthy W et al (2005) The performance of solar scan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma. Archiv Dermatol 141(11):1388–1396

    CrossRef  Google Scholar 

  15. Garnavi R, Aldeen M, Bailey J (2012) Computer-aided diagnosis of melanoma using border and wavelet-based texture analysis. IEEE Trans Inform Technol Biomed 16(6):1239–1252

    CrossRef  Google Scholar 

  16. Xie F, Fan H, Li Y, Jiang Z, Meng R, Bovik A (2016) Melanoma classification on dermoscopy images using a neural network ensemble model. IEEE Trans Med Imag 36(3):849–858

    CrossRef  Google Scholar 

  17. Yu L, Chen H, Dou Q, Qin J, Heng PA (2016) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imag 36(4):994–1004

    CrossRef  Google Scholar 

  18. 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, Innsbruck, pp 49–54

    Google Scholar 

  19. Satheesha T, Satyanarayana D, Prasad MG, Dhruve KD (2017) Melanoma is skin deep: a 3D reconstruction technique for computerized dermoscopic skin lesion classification. IEEE J Trans Eng Health Med 5:1–17

    CrossRef  Google Scholar 

  20. Xue C, Dou Q, Shi X, Chen H, Heng PA (2019) Robust learning at noisy labeled medical images: applied to skin lesion classification. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE, Venice, pp 1280–1283

    Google Scholar 

  21. Ghalejoogh GS, Kordy HM, Ebrahimi F (2020) A hierarchical structure based on stacking approach for skin lesion classification. Exp Syst Appl 145:113127

    CrossRef  Google Scholar 

  22. Mahbod A, Schaefer G, Wang C, Dorffner G, Ecker R, Ellinger I (2020) Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification. Comput Methods Program Biomed 193:105475

    CrossRef  Google Scholar 

  23. Kwasigroch A, Grochowski M, Mikołajczyk A (2020) Neural architecture search for skin lesion classification. IEEE Access 8:9061–9071

    CrossRef  Google Scholar 

  24. Harangi B (2018) Skin lesion classification with ensembles of deep convolutional neural networks. J Biomed Inform 86:25–32

    CrossRef  Google Scholar 

  25. Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29(9):2352–2449

    CrossRef  MathSciNet  Google Scholar 

  26. Yadav SS, Jadhav SM (2019) Deep convolutional neural network based medical image classification for disease diagnosis. J Big Data 6(1):113

    CrossRef  Google Scholar 

  27. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K et al (2017) ChexNet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv:1711.05225

  28. Lin M, Chen Q, Yan S (2013) Network in network. arXiv:1312.4400

  29. Huh M, Agrawal P, Efros AA (2016) What makes ImageNet good for transfer learning? arXiv:1608.08614

  30. Deng J, Dong W, Socher R, Li L, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition. IEEE, Florida, pp 248–255

    Google Scholar 

  31. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feed forward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics. Sardinia, Italy, pp 249–256

    Google Scholar 

  32. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980

  33. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems. Curran Associates Inc, Nevada, pp 1097–1105

    Google Scholar 

  34. 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. IEEE, Las Vegas, NV, pp 770–778

    Google Scholar 

  35. Li Y, Shen L (2018) Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18(2):556

    CrossRef  Google Scholar 

  36. Yang J, Xie F, Fan H, Jiang Z, Liu J (2018) Classification for dermoscopy images using convolutional neural networks based on region average pooling. IEEE Access 6:65130–65138

    CrossRef  Google Scholar 

  37. Sultana NN, Mandal B, Puhan NB (2018) Deep residual network with regularised fisher framework for detection of melanoma. IET Comput Vis 12(8):1096–1104

    CrossRef  Google Scholar 

  38. Serte S, Demirel H (2019) Gabor wavelet-based deep learning for skin lesion classification. Comput Biol Med 113:103423

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh

    Aishwariya Dutta

  2. Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh

    Md. Kamrul Hasan & Mohiuddin Ahmad

Authors
  1. Aishwariya Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Md. Kamrul Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohiuddin Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Md. Kamrul Hasan .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

    Prof. Mohammad Shorif Uddin

  2. Department of Applied Mathematics, South Asian University, New Delhi, India

    Dr. Jagdish Chand Bansal

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Dutta, A., Kamrul Hasan, M., Ahmad, M. (2021). Skin Lesion Classification Using Convolutional Neural Network for Melanoma Recognition. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0586-4_5

Download citation