Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification
importance 8/10
ai assisted
Cutaneous Melanoma Detection and Management dermatology / skin cancer stage: research
Problem
Early skin cancer detection requires accurate segmentation and classification of skin lesion images, where misdiagnosis can be fatal.
Why it matters
The abstract states early detection gives patients a good chance of cure, while wrong diagnosis leads to early death.
What the AI did
A hybrid deep learning pipeline with preprocessing (hair removal), lesion segmentation, and lesion classification stages was proposed.
Results
not stated in source
Impact
Proposes a method for enhanced lesion segmentation and classification; clinical impact not stated.
Record
| Venue | PLoS ONE |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | Saleem Mustafa, Arfan Jaffar, Muhammad Rashid, Sheeraz Akram, Sohail Masood Bhatti |
| Citations | 27 (OpenAlex, at import time) |
| References | 33 |
| Model or method | ResUNet++, modified AlexNet, Random Forest |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1371/journal.pone.0315120 |
| Open access | gold |
| Keywords | skin cancer, deep learning, lesion segmentation, classification, ResUNet++, random forest |
Importance rating
8
major — millions of lives, an industry or a field
Abstract calls skin cancer globally the most fatal disease; wrong diagnosis leads to early death while early detection is curable, indicating major mortality impact.