Different Skin Lesion Classification Techniques

Authors

  • Sameeksha Undale  Computer Science and Engineering, KLS Gogte Institute of Technology, Belagavi, Belgaum, Karnataka, India
  • D A Kulkarni  Computer Science and Engineering, KLS Gogte Institute of Technology, Belagavi, Belgaum, Karnataka, India

Keywords:

Classification, Deep convolutional neural network, Skin lesion, Image processing, Novel regularizer technique

Abstract

Skin cancer is most common group of human maliciousness, which is analyzed visually, with clinical screening and dermoscopic diagnosis, histo-pathological determination and at last biopsy. Melanoma is utmost hazardous and deadliest form of skin cancer. It should be identified and diagnosed at very early stage to cure t completely. To identify melanoma there are many non-invasive techniques available. This paper reviews existing non-invasive techniques used to identify melanoma. Deep convolutional neural networks (CNNs) can be utilized for object classification to obtain highly isolated results. This paper presents review on different available techniques to classify mages into Melanoma and Benign. The regularizer technique acts as binary classifier to separate them between benign or malignant lesions.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-04-30

Issue

Volume 7, Issue 2, March-April-2020

Section

Research Articles

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Copyright (c) IJSRSET

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sameeksha Undale, D A Kulkarni, " Different Skin Lesion Classification Techniques, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 2, pp.409-413, March-April-2020.