SynthesisSensors (Basel, Switzerland)2023
Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study.
Synthesis in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.BMC cancer · 2025Pooled it
- How is Artificial Intelligence Transforming the Skin Cancer Screening Pathway? An Umbrella Review.Research square · 2026Article
- Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction.Dermatopathology (Basel, Switzerland) · 2025Review
- ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases.Journal of imaging · 2025Article
- Transfer Learning-Based Ensemble of CNNs and Vision Transformers for Accurate Melanoma Diagnosis and Image Retrieval.Diagnostics (Basel, Switzerland) · 2025Article
- Contrastive Self-Supervised Ensemble Transfer Learning for Robust Skin Cancer Classification and Early Detection.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification.Scientific reports · 2025Article
- YOLOSAMIC: A Hybrid Approach to Skin Cancer Segmentation with the Segment Anything Model and YOLOv8.Diagnostics (Basel, Switzerland) · 2025Article
- A robust deep learning framework for multiclass skin cancer classification.Scientific reports · 2025Article
- Towards unbiased skin cancer classification using deep feature fusion.BMC medical informatics and decision making · 2025Article
- Minimal sourced and lightweight federated transfer learning models for skin cancer detection.Scientific reports · 2025Article
- Evaluation of an acne lesion detection and severity grading model for Chinese population in online and offline healthcare scenarios.Scientific reports · 2025Article
- AutoTFCNNY: A multi-instance neural network for enhanced early cancer detection using TCR data.PloS one · 2025Article
- Decoding skin cancer classification: perspectives, insights, and advances through researchers' lens.Scientific reports · 2024Review
- A Unified Multi-Task Learning Model with Joint Reverse Optimization for Simultaneous Skin Lesion Segmentation and Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- CAD-PsorNet: deep transfer learning for computer-assisted diagnosis of skin psoriasis.Scientific reports · 2024Article
- Mobile Application Development for Prepaid Water Meter Based on LC Sensor.Sensors (Basel, Switzerland) · 2024Article
- DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images.PloS one · 2024Article
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.
Funding
Abstract
Skin cancer is considered a dangerous type of cancer with a high global mortality rate. Manual skin cancer diagnosis is a challenging and time-consuming method due to the complexity of the disease. Recently, deep learning and transfer learning have been the most effective methods for diagnosing this deadly cancer. To aid dermatologists and other healthcare professionals in classifying images into melanoma and nonmelanoma cancer and enabling the treatment of patients at an early stage, this systematic literature review (SLR) presents various federated learning (FL) and transfer learning (TL) techniques that have been widely applied. This study explores the FL and TL classifiers by evaluating them in terms of the performance metrics reported in research studies, which include true positive rate (TPR), true negative rate (TNR), area under the curve (AUC), and accuracy (ACC). This study was assembled and systemized by reviewing well-reputed studies published in eminent fora between January 2018 and July 2023. The existing literature was compiled through a systematic search of seven well-reputed databases. A total of 86 articles were included in this SLR. This SLR contains the most recent research on FL and TL algorithms for classifying malignant skin cancer. In addition, a taxonomy is presented that summarizes the many malignant and non-malignant cancer classes. The results of this SLR highlight the limitations and challenges of recent research. Consequently, the future direction of work and opportunities for interested researchers are established that help them in the automated classification of melanoma and nonmelanoma skin cancers.
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.