Skip to main content
SpringerLink
Log in

A Convolutional Neural Network Framework for Accurate Skin Cancer Detection

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Skin diseases have become a challenge in medical diagnosis due to visual similarities. Although melanoma is the best-known type of skin cancer, there are other pathologies that are the cause of many death in recent years. The lack of large datasets is one of the main difficulties to develop a reliable automatic classification system. This paper presents a deep learning framework for skin cancer detection. Transfer learning was applied to five state-of-art convolutional neural networks to create both a plain and a hierarchical (with 2 levels) classifiers that are capable to distinguish between seven types of moles. The HAM10000 dataset, a large collection of dermatoscopic images, were used for experiments, with the help of data augmentation techniques to improve performance. Results demonstrate that the DenseNet201 network is suitable for this task, achieving high classification accuracies and F-measures with lower false negatives. The plain model performed better than the 2-levels model, although the first level, i.e. a binary classification, between nevi and non-nevi yielded the best outcomes.

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. http://www.image-net.org.

  2. https://www.cs.toronto.edu/~kriz/cifar.html.

  3. http://ufldl.stanford.edu/housenumbers/.

  4. http://cocodataset.org.

  5. http://host.robots.ox.ac.uk/pascal/VOC/.

References

  1. American Cancer Society I (ed) (2016) Cancer facts & figures. American Cancer Society, Atlanta

    Google Scholar 

  2. Asha Gnana Priya H, Anitha J, Poonima Jacinth J (2018) Identification of melanoma in dermoscopy images using image processing algorithms. In: 2018 international conference on control, power, communication and computing technologies, ICCPCCT 2018, pp 553–557

  3. Bakheet S (2017) An SVM framework for malignant melanoma detection based on optimized HOG features. Computation 5(1):1–13

    Google Scholar 

  4. Devassy B, Yildirim-Yayilgan S, Hardeberg J (2019) The impact of replacing complex hand-crafted features with standard features for melanoma classification using both hand-crafted and deep features. Adv Intell Syst Comput 868:150–159

    Google Scholar 

  5. Gao Z et al (2019) Privileged modality distillation for vessel border detection in intracoronary imaging. IEEE Trans Med Imaging 39(5):1524–1534

    Article  Google Scholar 

  6. Gao Z, Wang X, Sun S, Wu D, Bai J, Yin Y, Liu X, Zhang H, de Albuquerque VHC (2020) Learning physical properties in complex visual scenes: an intelligent machine for perceiving blood flow dynamics from static CT angiography imaging. Neural Netw 123:82–93

    Article  Google Scholar 

  7. Gao Z, Wu S, Liu Z, Luo J, Zhang H, Gong M, Li S (2019) Learning the implicit strain reconstruction in ultrasound elastography using privileged information. Med Image Anal 58:101534

    Article  Google Scholar 

  8. 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

  9. Hussain Z, Gimenez F, Yi D, Rubin D (2017) Differential data augmentation techniques for medical imaging classification tasks. In: AMIA annual symposium proceedings, vol 2017. American Medical Informatics Association, p 979

  10. Jafari MH, Karimi N, Nasr-Esfahani E, Samavi S, Soroushmehr SMR, Ward K, Najarian K (2016) Skin lesion segmentation in clinical images using deep learning. In: 2016 23rd international conference on pattern recognition (ICPR), pp 337–342

  11. Jafari MH, Nasr-Esfahani E, Karimi N, Soroushmehr SMR, Samavi S, Najarian K (2017) Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma. Int J Comput Assist Radiol Surg 12(6):1021–1030

    Article  Google Scholar 

  12. Jerant AF, Johnson JT, Sheridan C, Caffrey TJ (2000) Early detection and treatment of skin cancer. Am Fam Phys 62(2):357–368, 375–376, 381–382

  13. Khan MA, Javed MY, Sharif M, Saba T, Rehman A (2019) Multi-model deep neural network based features extraction and optimal selection approach for skin lesion classification. In: 2019 international conference on computer and information sciences (ICCIS). IEEE, pp 1–7

  14. Li J, Zhou G, Qiu Y, Wang Y, Zhang Y, Xie S (2019) Deep graph regularized non-negative matrix factorization for multi-view clustering. Neurocomputing 390:108–116

    Article  Google Scholar 

  15. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JA, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  Google Scholar 

  16. Liu N, Wan L, Zhang Y, Zhou T, Huo H, Fang T (2018) Exploiting convolutional neural networks with deeply local description for remote sensing image classification. IEEE Access 6:11215–11228

    Article  Google Scholar 

  17. Mobiny A, Singh A, Van Nguyen H (2019) Risk-aware machine learning classifier for skin lesion diagnosis. J Clin Med 8(8):1241

    Article  Google Scholar 

  18. Moldovan D (2019) Transfer learning based method for two-step skin cancer images classification. In: 2019 E-health and bioengineering conference (EHB), pp 1–4

  19. Nachbar F, Stolz W, Merkle T, Cognetta AB, Vogt T, Landthaler M, Bilek P, B-Falco O, Plewig G (1994) The ABCD rule of dermatoscopy: high prospective value in the diagnosis of doubtful melanocytic skin lesions. J Am Acad Dermatol 30(4):551–559

    Article  Google Scholar 

  20. Nida N, Irtaza A, Javed A, Yousaf M, Mahmood M (2019) Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. Int J Med Inf 124:37–48

    Article  Google Scholar 

  21. Nugroho AA, Slamet I, Sugiyanto (2019) Skins cancer identification system of HAMl0000 skin cancer dataset using convolutional neural network. AIP Conf Proc 2202(1):020039

  22. Oliveira RB, Papa JP, Pereira AS, Tavares JMR (2018) Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput Appl 29(3):613–636

    Article  Google Scholar 

  23. 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

  24. Pereira dos Santos F, Antonelli Ponti M (2018) Robust feature spaces from pre-trained deep network layers for skin lesion classification. In: 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, pp 189–196

  25. Ruela M, Barata C, Marques J, Rozeira J (2017) A system for the detection of melanomas in dermoscopy images using shape and symmetry features. Comput Methods Biomech Biomed Eng: Imaging Vis 5(2):127–137

    Google Scholar 

  26. Sae-Lim W, Wettayaprasit W, Aiyarak P (2019) Convolutional neural networks using mobileNet for skin lesion classification. In: 2019 16th international joint conference on computer science and software engineering (JCSSE), pp 242–247

  27. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520

  28. Shahin AH, Kamal A, Elattar MA (2018) Deep ensemble learning for skin lesion classification from dermoscopic images. In: 2018 9th Cairo international biomedical engineering conference (CIBEC). IEEE, pp 150–153

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

  30. Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: 31st AAAI conference on artificial intelligence

  31. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  32. Thurnhofer-Hemsi K, Domínguez E (2019) Analyzing digital image by deep learning for melanoma diagnosis. In: Proceedings of the 15th international work-conference on artificial neural networks (IWANN), pp 270–279

  33. Tschandl P, Rosendahl C, Kittler H (2018) The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5:180161

    Article  Google Scholar 

  34. Victor A, Ghalib M (2017) Automatic detection and classification of skin cancer. Int J Intell Eng Syst 10(3):444–451

    Google Scholar 

  35. Yadav V, Kaushik V (2018) Detection of melanoma skin disease by extracting high level features for skin lesions. Int J Adv Intell Paradig 11(3–4):397–408

    Article  Google Scholar 

  36. Yu L, Chen H, Dou Q, Qin J, Heng PA (2017) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imaging 36(4):994–1004

    Article  Google Scholar 

  37. Zhou T, Thung K, Zhu X, Shen D (2019) Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Hum Brain Mapp 40(3):1001–1016

    Article  Google Scholar 

Download references

Acknowledgements

This work is partially supported by the Ministry of Economy and Competitiveness of Spain under Grants TIN2016-75097-P and PPIT.UMA.B1.2017. It is also partially supported by the Ministry of Science, Innovation and Universities of Spain under Grant RTI2018-094645-B-I00, project name Automated detection with low-cost hardware of unusual activities in video sequences. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behavior agents by deep learning in low-cost video surveillance intelligent systems. All of them include funds from the European Regional Development Fund (ERDF). The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs used for this research. The authors acknowledge the funding from the Universidad de Málaga. Karl Thurnhofer-Hemsi (FPU15/06512) is funded by a PhD scholarship from the Spanish Ministry of Education, Culture and Sport under the FPU program.

Author information

Authors and Affiliations

  1. Department of Computer Languages and Computer Sciences, University of Málaga, Boulevar Louis Pasteur, 35, 29071, Málaga, Spain

    Karl Thurnhofer-Hemsi & Enrique Domínguez

  2. Biomedical Research Institute of Málaga (IBIMA), C/ Doctor Miguel Díaz Recio, 28, 29010, Málaga, Spain

    Karl Thurnhofer-Hemsi & Enrique Domínguez

Authors
  1. Karl Thurnhofer-Hemsi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Enrique Domínguez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karl Thurnhofer-Hemsi.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thurnhofer-Hemsi, K., Domínguez, E. A Convolutional Neural Network Framework for Accurate Skin Cancer Detection. Neural Process Lett 53, 3073–3093 (2021). https://doi.org/10.1007/s11063-020-10364-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-020-10364-y

Keywords

Search

Navigation