Skip to main content
SpringerLink
Log in

Skin Cancer Detection Based on Deep Learning Methods

  • Conference paper
  • First Online:
The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023 (AICV 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 164))

Abstract

One of the most common and dangerous types of tumors/cancer is skin cancer. It is an abnormal growth of the skin. Skin cancer that is not detected early spreads to other organs. In the era of deep learning technology and computer vision, determining whether or not a patient has cancer has become more accessible because the earlier the diagnosis is, the more it reduces the risk or prevents the patient’s death. Artificial intelligence has a lot of applications in healthcare, especially in skin cancer detection. In this paper, A convolutional neural network is used to make a diagnosis system more accurate in diagnosing whether the patient has cancer or not. HAM10000 is a Dataset which used to train the model, and it is used to classify between cancer and no cancer. MATLAB is a software tool we used when we train deep learning algorithms were used to make our diagnosis systems. To achieve the highest level of accuracy, two algorithms were used to optimize the parameters and apply them in GoogleNet, MobileNet-v2, NasNet mobile, SqueezNet, Darknet19, VGG16, Xception, ShuffleNet, Inception-v3, resnet18, resnet50 and nasnetlarge. Then the result of each of them is recorded, and the best algorithm is selected. Xception is used to make a diagnosis system with 96.66% accuracy to classify between cancer and no cancer.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. King, G., Zeng, L.: Replication data for: when can history be our guide? The pitfalls of counterfactual inference. Harv. Dataverse (2006)

    Google Scholar 

  2. Hanmer, M.J., Banks, A.J., White, I.K.: Replication data for: experiments to reduce the over-reporting of voting: a pipeline to the truth. Harv. Dataverse (2013)

    Google Scholar 

  3. Young, G.O.: Synthetic structure of industrial plastics. In: Peters, J. (ed.) Plastics, vol. 3, 2nd edn., pp. 15–64. McGraw-Hill, New York, NY, USA (1964)

    Google Scholar 

  4. Chen, W.-K.: Linear Networks and Systems, pp. 123–135. Wadsworth, Belmont, CA, USA (1993)

    Google Scholar 

  5. Ferlay, J., et al.: Cancer statistics for the year 2020: an overview. Int. J. Cancer (2021)

    Google Scholar 

  6. Apalla, Z., Nashan, D., Weller, R.B., Castellsaque, X.: Skin cancer: epidemiology, disease burden, pathophysiology, diagnosis and therapeutic approaches. Dermatol. Ther. 7(Suppl. 1), 5–19 (2017)

    Google Scholar 

  7. Mader, K.S.: Skin cancer MNIST: HAM10000. Kaggle. https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000. Accessed 2020

  8. Skin Cancer Facts & Statistics [Internet]. The Skin Cancer Foundation. https://www.skincancer.org/skincancer-information/skin-cancer-facts/. Accessed 22 June 2021

  9. Gavrilov, D., Lazarenko, L., Zakirov, E.: AI recognition in skin pathologies detection. In: 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) (2019)

    Google Scholar 

  10. Sabri, M.A., Filali, Y., El Khoukhi, H., Aarab, A.: Skin cancer diagnosis using an improved ensemble machine learning model. In: 2020 International Conference on Intelligent Systems and Computer Vision (ISCV) (2020)

    Google Scholar 

  11. Maglogiannis, I., Doukas, C.N.: Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans. Inf. Technol. Biomed. 13(5), 721–733 (2009)

    Article  Google Scholar 

  12. Hagerty, J., et al.: Deep learning and handcrafted method fusion: higher diagnostic accuracy for melanoma dermoscopy images. IEEE J. Biomed. Health Inform. 23(4), 1385–1391 (2019)

    Google Scholar 

  13. Hekler, A., et al.: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. Eur. J. Cancer 118, 91–96 (2019). https://doi.org/10.1016/j.ejca.2019.06.012

  14. Davis, L.E., Shalin, S.C., Tackett, A.J.: Current state of melanoma diagnosis and treatment. Cancer Biol. Ther. 20, 1366–1379 (2019)

    Article  Google Scholar 

  15. Bohr, A., Memarzadeh, K.: The rise of artificial intelligence in healthcare applications. Artif. Intell. Healthc., 25–60 (2020)

    Google Scholar 

  16. Pham, T.C., Luong, CM., Visani, M., Hoang, V.D.: Deep CNN and data augmentation for skin lesion classification. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds.) ACIIDS 2018. LNCS, vol. 10752, pp. 573–582. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75420-8_54

  17. Codella, N.C.F., et al.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev. (2017)

    Google Scholar 

  18. Aruhan: A medical support application for public based on convolutional neural network to detect skin cancer. In: 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), pp. 253–257 (2021). https://doi.org/10.1109/CEI52496.2021.9574496

  19. Zhou, D.-X.: Universality of deep convolutional neural networks. Appl. Comput. Harmon. Anal. 48(2), 787–794 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  20. Li, K.M., Li, E.C.: Skin lesion analysis towards melanoma detection via end-to-end deep learning of convolutional neural networks. arXiv preprint arXiv:1807.08332 (2018)

  21. Jaikishore, C.N., Udutalapally, V., Das, D.: AI driven edge device for screening skin lesion and its severity in peripheral communities. In: 2021 IEEE 18th India Council International Conference (INDICON) (2021)

    Google Scholar 

  22. Das, K., et al.: machine learning and its application in skin cancer. Int. J. Environ. Res. Public Health 18, 13409 (2021)

    Article  Google Scholar 

  23. LeCun, Y., et al.: Deep learning. Nature 521(7553), 436–44 (2015). https://doi.org/10.1038/nature14539

  24. Russakovsky, O. et al.: Imagenet large scale visual recognition challenge. IJCV (2015)

    Google Scholar 

  25. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)

    Google Scholar 

  26. Dan, B., Sun, X., Liu, L.: Diseases and pests identification of Lycium barbarum using SE-MobileNet V2 algorithm. In: 2019 12th International Symposium on Computational Intelligence and Design (ISCID), , pp. 121–125 (2019)

    Google Scholar 

  27. Çakmak, M., Tenekecı, M.E.: Melanoma detection from dermoscopy images using Nasnet mobile with transfer learning. In: 2021 29th Signal Processing and Communications Applications Conference (SIU), pp. 1–4 (2021)

    Google Scholar 

  28. Park, E., Cui, X., Nguyen, T.H.B., Kim, H.: Presentation attack detection using a tiny fully convolutional network. IEEE Trans. Inf. Forensics Secur. 14(11), 3016–3025 (2019). https://doi.org/10.1109/TIFS.2019.2907184

    Article  Google Scholar 

  29. Setiawan, W., Purnama, A.: Tobacco leaf images clustering using DarkNet19 and K-means. In: 2020 6th Information Technology International Seminar (ITIS), pp. 269–273 (2020)

    Google Scholar 

  30. Li, Y., Lv,C.: SS-YOLO: an object detection algorithm based on YOLOv3 and ShuffleNet. In: 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 769–772 (2020)

    Google Scholar 

  31. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)

    Google Scholar 

  32. Al Husaini, M.A.S., Habaebi, M.H., Gunawan, T.S., Islam, M.R., Hameed, S.A.: Automatic breast cancer detection using inception V3 in thermography. In: 2021 8th International Conference on Computer and Communication Engineering (ICCCE), pp. 255–258 (2021)

    Google Scholar 

  33. Moid, M.A., Ajay Chaurasia, M.: Transfer learning-based plant disease detection and diagnosis system using Xception. In: 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 1–5 (2021)

    Google Scholar 

  34. Aung, H., Bobkov, A.V., Tun, N.L.: Face detection in real time live video using yolo algorithm based on VGG16 convolutional neural network. In: 2021 International Conference on Industrial Engineering, Applications (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Egyptian Chinese College for Applied Technology, Ismailia, 41511, Egypt

    Sara Shaaban, Hanan Atya, Heba Mohammed, Ahmed Sameh & Kareem Raafat

  2. Electrical Engineering Department, Suez Canal University, Ismailia, 41511, Egypt

    Ahmed Magdy

Authors
  1. Sara Shaaban
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanan Atya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Heba Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed Sameh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kareem Raafat
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ahmed Magdy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kareem Raafat .

Editor information

Editors and Affiliations

  1. Faculty of Computer and AI, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Ahmad Taher Azar

  4. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    KC Santosh

  5. Vardhaman College of Engineering, Hyderabad, Telangana, India

    M. A. Jabbar

  6. Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, Poland

    Adam Słowik

  7. Department of Computer Science, Avinashilingam University for Women, Coimbatore, Tamil Nadu, India

    Parthasarathy Subashini

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shaaban, S., Atya, H., Mohammed, H., Sameh, A., Raafat, K., Magdy, A. (2023). Skin Cancer Detection Based on Deep Learning Methods. In: Hassanien, A.E., et al. The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 164. Springer, Cham. https://doi.org/10.1007/978-3-031-27762-7_6

Download citation

Publish with us

Policies and ethics

Search

Navigation