体育赛事投注记录

advertisement

Skin Cancer Classification Using Convolution Neural Networks

  • Subasish MohapatraEmail author
  • N. V. S. Abhishek
  • Dibyajit Bardhan
  • Anisha Ankita Ghosh
  • Subhadarshini Mohanty
Conference paper
  • 22 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 127)

Abstract

The incidence of skin cancers has been increasing over the past decades at an alarming rate. Right now, somewhere in the range of 2 and 3 million non-melanoma skin diseases and 132,000 melanoma skin malignant growths happen all-inclusive every year. One in each three diseases analyzed is skin malignant growth. As ozone levels keep being drained, the air loses increasingly more of its defensive channel capacity, and progressively sun-oriented UV radiation arrives at the Earth’s surface. It is assessed that a 10 percent decline in ozone levels will bring about an extra 300,000 skin malignant growth cases. Hence, skin cancer today poses a serious threat to mankind. One of the major reasons for skin cancer fatalities is the absence of an early diagnosis. When detected early, skin cancer survival exceeds 95%. Therefore, to facilitate the early diagnosis of skin cancer, we propose this solution—a machine learning model trained on HAM10000 dataset (a large collection of multi-source dermatoscopic images of common pigmented skin lesions体育赛事投注记录) using convolutional neural networks (CNN), to classify a given skin lesion image into various cancerous (or non-cancerous) skin conditions. This model incorporated into an online platform will enable doctors and laboratory technologists to know the three highest probability diagnoses for a given skin lesion. Hence, the model will be of immense help in quickly identifying high priority patients and speeding up the procedural workflow.

Keywords

Convolutional neural networks (CNN) Machine learning Skin lesion 

References

  1. 1.
    Masood A, Al-Jumaily AA (2013) Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int J Biomed Imaging
  2. 2.
    Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31(4–5):198–211
  3. 3.
    Mehta P, Shah B (2016) Review on techniques and steps of computer aided skin cancer diagnosis. Proc Comput Sci 85:309–316
  4. 4.
    Abdel-Zaher AhM, Eldeib AM (2016) Breast cancer classification using deep belief networks. Expert Syst Appl 46:139–144
  5. 5.
    Pathan SK, Prabhu G, Siddalingaswamy PC (2018) Techniques and algorithms for computer aided diagnosis of pigmented skin lesions—a review. Biomed Signal Process Control 39:237–262
  6. 6.
    The Skin Cancer Foundation (2018) Skin cancer information. Available: . Accessed 25 Dec 2018
  7. 7.
    Hameed N, Ruskin A, Abu Hassan K, Hossain MA (2016) A comprehensive survey on image-based computer aided diagnosis systems for skin cancer. In: 2016 10th international conference on software, knowledge, information management & applications (SKIMA). IEEE, New York, pp 205–214
  8. 8.
    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
  9. 9.
    Nylund A (2016) To be, or not to be Melanoma: convolutional neural networks in skin lesion classification
  10. 10.
    Ali AA, Al-Marzouqi H (2017) Melanoma detection using regular convolutional neural networks. In: 2017 International conference on electrical and computing technologies and applications (ICECTA). IEEE, New York, pp 1–5
  11. 11.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
  12. 12.
    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

Copyright information

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

Authors and Affiliations

  • Subasish Mohapatra
    • 1
    Email author
  • N. V. S. Abhishek
    • 1
  • Dibyajit Bardhan
    • 1
  • Anisha Ankita Ghosh
    • 1
  • Subhadarshini Mohanty
    • 1
  1. 1.CETBhubaneswarIndia

Personalised recommendations