Skip to main content

Skin Cancer Multiclass Classification Using Weighted Ensemble Model

  • Conference paper
  • First Online:
Intelligent Computing and Networking (IC-ICN 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 699))

Included in the following conference series:

  • 475 Accesses

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Similar content being viewed by others

References

  1. World Health Organization (2020) Globocan 2020. Estimated cancer incidence, mortality, and prevalence worldwide in 2020. https://gco.iarc.fr/today/data/factsheets/cancers/6-Oesophagus-fact-sheet.pdf

  2. Dong J, Thrift AP (2017) Alcohol, smoking and risk of oesophago-gastric cancer. Best Pract Res Clin Gastroenterol 31(5):509–517

    Article  Google Scholar 

  3. Chlosser RW (2006) The role of systematic reviews in evidence-based practice, research and development. Focus 15. https://ktdrr.org/ktlibrary/articles_pubs/ncddrwork/focus/focus15

  4. World Health Organization (2017) More can be done to restrict sunbeds to pre- vent increasing rates of skin cancer. https://www.who.int/phe/news/sunbeds-skin-cancer/en/

  5. NHS (2020a) How does the sun and UV cause cancer?. https://www.nhs.uk/conditions/melanoma-skin-cancer/causes/

  6. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

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

    Google Scholar 

  8. 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, p 770–778

    Google Scholar 

  9. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 1251–1258

    Google Scholar 

  10. 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, p 4700–4708

    Google Scholar 

  11. Popescu D, El-Khatib M, ElKhatib H, Ichim L (2022) New trends in melanoma detection using neural networks: a systematic review. Sensors

    Google Scholar 

  12. Shen X, Wei L, Tang S (2022) Dermoscopic image classification method using an ensemble of fine-tuned convolutional neural networks. Sensors

    Google Scholar 

  13. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118

    Article  Google Scholar 

  14. Kawahara J, BenTaieb A, Hamarneh G (2016) Deep features to classify skin lesions. In: 2016 IEEE 13th international symposium on biomedical imaging. IEEE, pp 1397–1400

    Google Scholar 

  15. Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492–1500

    Google Scholar 

  16. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition., pp 7132–7141

    Google Scholar 

  17. Ain QU, Al-Sahaf H, Xue B, Zhang M (2020) Generating knowledge-guided discriminative features using genetic programming for Melanoma detection. IEEE Trans Emerging Top Comput Intell

    Google Scholar 

  18. Mohamed EH, El-Behaidy WH (2019) Enhanced skin lesions classification using deep convolutional networks. In: 2019 Ninth international conference on intelligent computing and information systems. IEEE, pp 180–188

    Google Scholar 

  19. Steppan J, Hanke S (2021) Analysis of skin lesion images with deep learning. arXiv:2101.03814

  20. 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 

  21. 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 Programs Biomed 105475

    Google Scholar 

  22. 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. IEEE, pp 242–247

    Google Scholar 

  23. Chaturvedi SS, Gupta K, Prasad PS (2020) Skin lesion analyser: an efficient seven-way multi-class skin cancer classification using MobileNet. In: International conference on advanced machine learning technologies and applications. Springer, pp 165–176

    Google Scholar 

  24. Ju L, Wang X, Wang L, Mahapatra D, Zhao X, Harandi M, Drummond T, Liu T, Ge Z (2021) Improving medical image classification with label noise using dual-uncertainty estimation. 2103.00528

    Google Scholar 

  25. Jinnai S, Yamazaki N, Hirano Y, Sugawara Y, Ohe Y, Hamamoto R (2020) The development of a skin cancer classification system for pigmented skin lesions using deep learning. Biomolecules 10(8):1123

    Article  Google Scholar 

  26. De Hertog SA, Wensveen CA, Bastiaens MT, Kielich CJ, Berkhout MJ, Westendorp RG, Vermeer BJ, Bavinck JNB (2001) Relation between smoking and skin cancer. J Clin Oncol 19

    Google Scholar 

  27. Acosta MFJ, Tovar LYC, Garcia-Zapirain MB, Percybrooks W (2021) Melanoma diagnosis using deep learning techniques on dermatoscopic images. BMC Med Imaging 21

    Google Scholar 

  28. Hasan M, Elahi ME, Alam MA (2021) Dermoexpert: skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation. medRxiv

    Google Scholar 

  29. Heidari M, Mirniaharikandehei S, Khuzani AZ, Danala G, Qiu Y, Zheng B (2020) Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. Int J Med Inf 144:104284

    Article  Google Scholar 

  30. Mikołajczyk A, Grochowski M (2018) Data augmentation for improving deep learning in image classification problem. In: 2018 International interdisciplinary PhD workshop. IEEE, pp 117–122

    Google Scholar 

  31. Alpaydin E (2020) Introduction to machine learning. The MIT Press, Cambridge, MA, USA

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. R. Nalamwar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nalamwar, S.R., Neduncheliyan, S. (2023). Skin Cancer Multiclass Classification Using Weighted Ensemble Model. In: Balas, V.E., Semwal, V.B., Khandare, A. (eds) Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_12

Download citation

Publish with us

Policies and ethics