Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review
Problem
Skin lesion diagnosis is a growing research area, and the paper reviews the diagnostic accuracy of computer-aided ML/DL systems developed over the last five years.
Why it matters
Accurate skin lesion diagnosis affects patient outcomes, and computer-aided systems could support clinicians.
What the AI did
A systematic review synthesizing and evaluating evidence from 53 traditional machine learning and 49 deep learning articles on skin lesion segmentation and classification, comparing methods and results.
Results
not stated in source
Impact
The review compares 102 studies and identifies evaluation challenges including small datasets, ad hoc image selection, and racial bias.
Record
| Venue | Diagnostics |
|---|---|
| Published | 2021 |
| Work type | review |
| Authors | Mohamed A. A. Kassem, Khalid Mohamed Hosny, Robertas Damaševičius, Mohamed Meselhy Eltoukhy |
| Citations | 270 (OpenAlex, at import time) |
| References | 219 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/diagnostics11081390 |
| Open access | gold |
| Keywords | skin lesion, machine learning, deep learning, systematic review, computer-aided diagnosis, racial bias |
Importance rating
real but narrower advance
Reviews 102 studies of computer-aided skin lesion diagnosis; identifies evaluation challenges including small datasets and racial bias; growing area supporting clinicians.