ReviewDiagnostics (Basel, Switzerland)2023
Skin Cancer Detection Using Deep Learning-A Review.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 140 citations in OpenAlex.
- Personalized digital pipeline for basal cell carcinoma delineation using hyperspectral imaging.Biomedical optics express · 2026Article
- Attention guided fair artificial intelligence modeling for skin cancer diagnosis.NPJ digital medicine · 2026Article
- Smart medical system integrating clinical workflows for robust skin cancer detection across heterogeneous pathologies.Scientific reports · 2026Article
- Text guided cross attentive multimodal learning with visual feature modulation for automated skin lesion detection.Scientific reports · 2026Article
- Modelling of hybrid deep ensemble learning based skin lesion detection using FCNN denoising and inception-dilated ResNetV2.Scientific reports · 2026Article
- Skin Tone in Hyperspectral Imaging and Its Implications for Fairness in AI.Journal of biophotonics · 2026Article
- Transferosomes as Advanced Nanocarriers for Enhanced Drug Delivery in Skin Cancer Therapy: A Comprehensive Review.Applied biochemistry and biotechnology · 2026Review
- Thyroid cancer detection and classification using spectral imaging and artificial intelligence.Scientific reports · 2026Article
- Browser-Based Multi-Cancer Classification Framework Using Depthwise Separable Convolutions for Precision Diagnostics.Diagnostics (Basel, Switzerland) · 2025Article
- A methodology for developing dermatological datasets: lessons from retrospective data collection for AI-based applications.BMC medical research methodology · 2025Article
- A highly accurate risk factor-based XGBoost multiethnic model for identifying patients with skin cancer.Nature communications · 2025Article
- Article
- Skin region images extracted from 3D total body photographs for lesion detection.Scientific data · 2025Article
- Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model.Scientific reports · 2025Article
- Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review.NPJ digital medicine · 2025Article
- A multi-layered defense against adversarial attacks in brain tumor classification using ensemble adversarial training and feature squeezing.Scientific reports · 2025Article
- Investigation of Binary and Multiclass Classification Performance of Skin Cancer Images Using Transfer Learning Methods.Journal of clinical practice and research · 2025Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- Antitumor Effects of Quercetin and Luteolin in A375 Cutaneous Melanoma Cell Line Are Mediated by Upregulation of P-ERK, c-Myc, and the Upstream GPER.Life (Basel, Switzerland) · 2025Article
- Skin cancer detection using dermoscopic images with convolutional neural network.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
Skin cancer is one the most dangerous types of cancer and is one of the primary causes of death worldwide. The number of deaths can be reduced if skin cancer is diagnosed early. Skin cancer is mostly diagnosed using visual inspection, which is less accurate. Deep-learning-based methods have been proposed to assist dermatologists in the early and accurate diagnosis of skin cancers. This survey reviewed the most recent research articles on skin cancer classification using deep learning methods. We also provided an overview of the most common deep-learning models and datasets used for skin cancer classification.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.