A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasets

Chengdong Yao
2/19/2023
cs.CV

No confident code implementation found for this paper.

Abstract

Skin cancer is the most common type of cancer. Specifically, melanoma is the cause of 75% of skin cancer deaths, although it is the least common skin cancer. Better detection of melanoma could have a positive impact on millions of people. The ISIC archive contains the largest publicly available collection of dermatoscopic images of skin lesions. In this research, we investigate the efficacy of applying advanced deep learning techniques in computer vision to identify melanoma in images of skin lesions. Through reviewing previous methods, including pre-trained models, deep-learning classifiers, transfer learning, etc., we demonstrate the applicability of the popular deep learning methods on critical clinical problems such as identifying melanoma. Finally, we proposed a processing flow with a validation AUC greater than 94% and a sensitivity greater than 90% on ISIC 2016 - 2020 datasets.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

A Comparative Analysis Towards Melanoma Classification Using Transfer Learning by Analyzing Dermoscopic Images

92% match2023

Comparison of Deep Learning and Machine Learning Models and Frameworks for Skin Lesion Classification

89% match2022

An Interpretable Deep Learning Approach for Skin Cancer Categorization

89% match1 repo112023

Towards Fairness in AI for Melanoma Detection: Systemic Review and Recommendations

89% match2024

Skin Cancer Detection utilizing Deep Learning: Classification of Skin Lesion Images using a Vision Transformer

88% match2024

Using Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing Interpretability and Transparency in Deep Learning Models for Skin Cancer Classification

88% match2025

Cite this paper

@article{yao2023comprehensive,
  title  = {A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasets},
  author = {Chengdong Yao},
  year   = {2023},
  eprint = {2302.09528},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.09528v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2302.09528.svg)](https://codeofpaper.com/paper/2302.09528)

Discussion