ArticleDiagnostics (Basel, Switzerland)2023
Diagnosing Melanomas in Dermoscopy Images Using Deep Learning.
Article 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 8 papers.
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
8 citing papers in PubMed.
- Deep Ensemble Learning for Multiclass Skin Lesion Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Advances in computer vision and deep learning-facilitated early detection of melanoma.Briefings in functional genomics · 2025Review
- Automated Brain Tumor segmentation using hybrid YOLO and SAM.Current medical imaging · 2025Article
- Artificial Intelligence in the Non-Invasive Detection of Melanoma.Life (Basel, Switzerland) · 2024Review
- Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review.Frontiers in medicine · 2024Article
- A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework.Healthcare (Basel, Switzerland) · 2023Article
- Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
When it comes to skin tumors and cancers, melanoma ranks among the most prevalent and deadly. With the advancement of deep learning and computer vision, it is now possible to quickly and accurately determine whether or not a patient has malignancy. This is significant since a prompt identification greatly decreases the likelihood of a fatal outcome. Artificial intelligence has the potential to improve healthcare in many ways, including melanoma diagnosis. In a nutshell, this research employed an Inception-V3 and InceptionResnet-V2 strategy for melanoma recognition. The feature extraction layers that were previously frozen were fine-tuned after the newly added top layers were trained. This study used data from the HAM10000 dataset, which included an unrepresentative sample of seven different forms of skin cancer. To fix the discrepancy, we utilized data augmentation. The proposed models outperformed the results of the previous investigation with an effectiveness of 0.89 for Inception-V3 and 0.91 for InceptionResnet-V2.
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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.