ArticleSoft computing2023
RETRACTED ARTICLE: DenseNet-II: an improved deep convolutional neural network for melanoma cancer detection.
Article in Soft computing, 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 It has been retracted, and should not be counted.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 11 papers, 1 of them a synthesis that pooled it.
What it found
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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
11 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
- Automated Melanocytic Lesion Classification: Capsule Networks Trained With Synthetic Images Can Outperform Networks Trained With Real Images.The Australasian journal of dermatology · 2026Article
- Hybrid vision transformer and graph neural network model with region-adaptive attention for enhanced skin cancer prediction.Scientific reports · 2026Article
- Modified EfficientNet-B0 Architecture Optimized with Quantum-Behaved Algorithm for Skin Cancer Lesion Assessment.Diagnostics (Basel, Switzerland) · 2025Article
- Research on bamboo strip density control technology based on deep learning.Scientific reports · 2025Article
- Deep learning approach with ConvNeXt-SE-attn model for in vitro oral squamous cell carcinoma and chemotherapy analysis.MethodsX · 2025Article
- Advances in computer vision and deep learning-facilitated early detection of melanoma.Briefings in functional genomics · 2025Review
- Intelligent smart sensing with ResNet-PCA and hybrid ML-DNN for sustainable and accurate plant disease detection.Frontiers in plant science · 2025Article
- Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Deep learning-based radiomic nomograms for predicting Ki67 expression in prostate cancer.BMC cancer · 2023Article
- MAC-ResNet: Knowledge Distillation Based Lightweight Multiscale-Attention-Crop-ResNet for Eyelid Tumors Detection and Classification.Journal of personalized medicine · 2022Article
Corrections and comments
- Retraction · 2026-01-21Compromised Peer Review · Concerns/Issues about Referencing/Attributions · Investigation by Journal/Publisher · Rogue Editor · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research in the field of medicine and relevant studies evince that melanoma is one of the deadliest cancers. It defines precisely that the condition develops due to uncontrolled growth of melanocytic cells. The current trends in any disease detection revolve around the usage of two main categories of models; these are general machine learning models and deep learning models. Further, the experimental analysis of melanoma has an additional requirement of visual records like dermatological scans or normal camera lens images. This further accentuates the need for a more accurate model for melanoma detection. In this work, we aim to achieve the same, primarily by the extensive usage of neural networks. Our objective is to propose a deep learning CNN framework-based model to improve the accuracy of melanoma detection by customizing the number of layers in the network architecture, activation functions applied, and the dimension of the input array. Models like Resnet, DenseNet, Inception, and VGG have proved to yield appreciable accuracy in melanoma detection. However, in most cases, the dataset was classified into malignant or benign classes only. The dataset used in our research provides seven lesions; these are
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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.