ArticleSensors (Basel, Switzerland)2022
On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 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 Early Detection of Skin Cancer Using a CNN-ViT-Attention-Based Hybrid Model.Biomedicines · 2026Article
- GSARC: a group shuffle activated redundant connection framework for post-hoc feature space adaptation in skin lesion classification on the HAM10000 dataset.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.Scientific reports · 2025Article
- A hybrid fused-KNN based intelligent model to access melanoma disease risk using indoor positioning system.Scientific reports · 2025Article
- Next-generation approach to skin disorder prediction employing hybrid deep transfer learning.Frontiers in big data · 2025Article
- Article
- Article
- Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review.Journal of imaging · 2024Review
- Fractional differentiation based image enhancement for automatic detection of malignant melanoma.BMC medical imaging · 2024Article
- Integration of Localized, Contextual, and Hierarchical Features in Deep Learning for Improved Skin Lesion Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks.Diagnostics (Basel, Switzerland) · 2024Article
- An efficient multi-class classification of skin cancer using optimized vision transformer.Medical & biological engineering & computing · 2024Article
- DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework.Scientific reports · 2024Article
- Alginate Nanoparticles ContainingInternational journal of biomaterials · 2024Article
- MpoxNet: dual-branch deep residual squeeze and excitation monkeypox classification network with attention mechanism.Frontiers in cellular and infection microbiology · 2024Article
- Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System.International journal of biomedical imaging · 2024Article
- DermAI 1.0: A Robust, Generalized, and Novel Attention-Enabled Ensemble-Based Transfer Learning Paradigm for Multiclass Classification of Skin Lesion Images.Diagnostics (Basel, Switzerland) · 2023Article
- A Comparative Analysis of Skin Cancer Detection Applications Using Histogram-Based Local Descriptors.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Skin cancer (melanoma and non-melanoma) is one of the most common cancer types and leads to hundreds of thousands of yearly deaths worldwide. It manifests itself through abnormal growth of skin cells. Early diagnosis drastically increases the chances of recovery. Moreover, it may render surgical, radiographic, or chemical therapies unnecessary or lessen their overall usage. Thus, healthcare costs can be reduced. The process of diagnosing skin cancer starts with dermoscopy, which inspects the general shape, size, and color characteristics of skin lesions, and suspected lesions undergo further sampling and lab tests for confirmation. Image-based diagnosis has undergone great advances recently due to the rise of deep learning artificial intelligence. The work in this paper examines the applicability of raw deep transfer learning in classifying images of skin lesions into seven possible categories. Using the HAM1000 dataset of dermoscopy images, a system that accepts these images as input without explicit feature extraction or preprocessing was developed using 13 deep transfer learning models. Extensive evaluation revealed the advantages and shortcomings of such a method. Although some cancer types were correctly classified with high accuracy, the imbalance of the dataset, the small number of images in some categories, and the large number of classes reduced the best overall accuracy to 82.9%.
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.