A Deep Learning Approach Based on Explainable Artificial Intelligence for Skin Lesion Classification
importance 6/10
ai assisted
Cutaneous Melanoma Detection and Management dermatology stage: research
Problem
Skin lesions look highly similar in early stages of skin cancer, causing delayed diagnosis.
Why it matters
Early and accurate skin cancer diagnosis affects patient outcomes.
What the AI did
An explainable AI deep-learning model classified eight skin lesion types while providing interpretable outputs so dermatologists can validate decisions.
Results
94.47% accuracy and 93.57% precision on the ISIC 2019 dataset; recall and F1 truncated in abstract.
Impact
Benchmark-validated classifier to support early dermatologist diagnosis; no clinical deployment stated.
Record
| Venue | IEEE Access |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Natasha Nigar, Muhammad Umar, Muhammad Kashif Shahzad, Shahid Islam, Douhadji Abalo |
| Citations | 178 (OpenAlex, at import time) |
| References | 56 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1109/access.2022.3217217 |
| Open access | gold |
| Keywords | explainable AI, deep learning, skin cancer, ISIC 2019, classification |
Importance rating
6
significant — thousands to millions affected
Early accurate skin cancer diagnosis affects patient outcomes across a common disease area; abstract reports benchmark accuracy but no population scale or clinical deployment.