ArticleHeliyon2023
An optimal method for melanoma detection from dermoscopy images using reinforcement learning and support vector machine optimized by enhanced fish migration optimization algorithm.
Article in Heliyon, 2023. 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 It has been retracted, and should not be counted.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 1 paper.
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
1 citing paper in PubMed.
- A deep learning-based radiomics model for noninvasive diagnosis of melanoma.Melanoma research · 2026Article
Corrections and comments
- Retraction · 2025-12-16Author Unresponsive · Concerns/Issues about Referencing/Attributions · Investigation by Journal/Publisher · Objections by Author(s) · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/1FF7F94D91F38BAC2F8216F6D5D4E3
- Retracted
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection of melanoma is crucial in preventing death from this fatal skin cancer. Therefore, it would be valuable to develop a method that facilitates this process. The diagnosis of melanoma typically involves an invasive form of testing called a biopsy, as well as non-invasive intelligent approaches to diagnosis. In the present study a recent research, a novel approach has been developed for the optimal detection of melanoma cancer. The method uses reinforcement learning for segmenting the skin regions, followed by the extraction and selection of useful features using the Enhanced Fish Migration Optimizer (EFMO) algorithm. The outcomes get categorized on the basis of an optimized SVM on the basis of the EFMO algorithm. The recommended approach has been certified by applying it to the SIIM-ISIC dataset of Melanoma and comparing it with 12 other approaches. Simulations illustrated that the proposed method delivered the finest values compared to the others.
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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.