ArticlePloS one2025
RETRACTED: Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 9 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
9 citing papers in PubMed.
- Class-balanced dermoscopic lesion segmentation using MoG-LISA and optimized Swin-UNet via the GM-FDE framework.iScience · 2026Article
- Liver cancer segmentator: Metadata-guided confidence scoring for reliable segmentation of colorectal liver metastases in CT.Computer methods and programs in biomedicine · 2026Article
- Alzheimer's disease prediction algorithm based on hippocampal longitudinal hybrid morphological features.Quantitative imaging in medicine and surgery · 2026Article
- Retraction: Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning.PloS one · 2026Article
- Application of YOLO-v7 and YOLO-v8 Transfer Learning Models in Breast Lesion Classification and DiagnosisCurrent medical imaging · 2026Article
- A 3T Magnetic Resonance Fractional Order Calculus Model for the Diagnosis and Grading of Endometrial Cancer.Current medical imaging · 2026Article
- Enhanced Feature Extraction for Detection and Classification of Kidney Abnormalities.Current medical imaging · 2026Article
- A dual-branch network for lesion segmentation in medical images using state space models.Quantitative imaging in medicine and surgery · 2025Article
- Improving early liver metastasis detection in colorectal cancer using a weighted ensemble of ResNet50 and swin transformer: a KHCC study.Frontiers in big data · 2025Article
Corrections and comments
- Retraction · 2026-02-05Compromised Peer Review · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Objections by Author(s) · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Skin cancer is a severe and rapidly advancing condition that can be impacted by multiple factors, including alcohol and tobacco use, allergies, infections, physical activity, exposure to UV light, viral infections, and the effects of climate change. While the steep death tolls continue rising at an alarming rate, lack of symptoms recognition and its preventive measures further worsen the case. In this article, we employ the ISBI-2017 dataset to present an improved FrCN-based hybrid image segmentation method with U-Net to improve detection performance. This paper proposes a hybrid approach using the FrCN-(U-Net) image segmentation technique to enhance results compared to an advanced method for detecting skin cancer types, such as Benign or Melanoma. The classification phase is then handled using the R-CNN algorithm. Our model shows better performance in both training and testing accuracy than any other existing approaches. The results show that the combined method is effective in enhancing early disease diagnosis, which in turn improves treatment outcomes and prognosis. This paper presents an alternative technique for skin cancer detection, which can serve as a guide for clinical practices and public health strategies on how to lower skin-cancer-related deaths.
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.