# Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/deep-learning-techniques-for-skin-lesion-analysis-and-melanoma-cancer-detection/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Adekanmi Adeyinka Adegun,Serestina Viriri |
| Citations | 320 |
| DOI | 10.1007/s10462-020-09865-y |
| Fields | Biochemistry, Genetics and Molecular Biology,Computer Science,Medicine |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3037436903 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Adekanmi Adeyinka Adegun](https://scholariq.org/researchers/adekanmi-adeyinka-adegun/)
- [Serestina Viriri](https://scholariq.org/researchers/serestina-viriri/)

## Paper journal

- [Artificial Intelligence Review](https://scholariq.org/journals/artificial-intelligence-review/)

## Paper primary topic

- [Cutaneous Melanoma Detection and Management](https://scholariq.org/topics/cutaneous-melanoma-detection-and-management/)

## Paper topics

- [Cutaneous Melanoma Detection and Management](https://scholariq.org/topics/cutaneous-melanoma-detection-and-management/)
- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)
- [Melanoma and MAPK Pathways](https://scholariq.org/topics/melanoma-and-mapk-pathways/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.