Classification Of Skin Cancer By Means Of Transfer Learning Models

Authors

  • Ji Zhe Lee Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pahang, Malaysia.
  • Anwar P. P. Abdul Majeed Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pahang, Malaysia.

DOI:

https://doi.org/10.15282/mekatronika.v3i2.7393

Keywords:

Skin Cancer, CNN, Classification, Transfer Learning, Machine Learning

Abstract

Skin cancer is a disease of human skin affected with abberrant or damaged cell and that lead to the formation of tumours. Skin cancer can be mainly classified into melanoma and non-melanoma, where melanoma is more deadly if misdiagnosis at the early stage. Traditional way of skin cancer classification required dermatologist to classify the cancer based on CT-scan, MRI or X-ray, which may promote risks of misdiagnosis. Hence deep learning is introduced to carry out the image feature extraction for the classification tasks by using the ISIC dataset. With the aids of InceptionV3 on different machine learning model, the skin cancer classification can be carry out by Artificial Intelligence. As a result of this study, Logistic Regression achieved overall classification accuracy of 78.3%, proven it has the ability to classify skin cancer based on skin lesion images

Downloads

Published

2021-12-14

How to Cite

[1]
J. Z. Lee and A. P. P. Abdul Majeed, “Classification Of Skin Cancer By Means Of Transfer Learning Models”, MEKATRONIKA, vol. 3, no. 2, pp. 77–81, Dec. 2021.

Issue

Section

Original Article

Most read articles by the same author(s)

1 2 > >>