ArticleJournal of clinical medicine2023
Classification of Skin Cancer Using Novel Hyperspectral Imaging Engineering via YOLOv5.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study.Sensors (Basel, Switzerland) · 2023Pooled it
- Deep Learning-Assisted Classification of Urinary Red Blood Cell Morphology for Glomerular Hematuria Screening: A Pilot Study.Journal of clinical laboratory analysis · 2026Article
- Article
- Correlation analysis between tongue hyperspectral imaging and tongue coating microbiota and feasibility study for early screening of type 2 diabetes mellitus status.Frontiers in endocrinology · 2026Article
- Article
- Artificial Intelligence-Assisted Dermatologic Screening: Epidemiology and Clinical Features of Basal Cell Carcinoma, Squamous Cell Carcinoma, Seborrheic Keratosis and Actinic Keratosis.Bioengineering (Basel, Switzerland) · 2025Review
- Dimensionality reduction in hyperspectral imaging using standard deviation-based band selection for efficient classification.Scientific reports · 2025Article
- MST-AI: Skin Color Estimation in Skin Cancer Datasets.Journal of imaging · 2025Article
- Small intestinal bleeding prediction by spectral reconstruction through band selection.Journal of biomedical optics · 2025Article
- Low Intratumoral CD200 Protein Expression in Primary Merkel Cell Carcinoma Is a Strong Predictor for Disease Relapse.Cancers · 2025Article
- [An efficient and lightweight skin pathology detection method based on multi-scale feature fusion using an improved RT-DETR model].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Training State-of-the-Art Deep Learning Algorithms with Visible and Extended Near-Infrared Multispectral Images of Skin Lesions for the Improvement of Skin Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluation of Band Selection for Spectrum-Aided Visual Enhancer (SAVE) for Esophageal Cancer Detection.Journal of Cancer · 2025Article
- Enhancing dermatological diagnosis for differentiating actinic from seborrheic keratosis using deep learning model.Frontiers in medicine · 2025Article
- Article
- Global, regional, national prevalence, mortality, and disability-adjusted life-years of cutaneous squamous cell carcinoma and trend analysis from 1990 to 2021 and prediction to 2045.Frontiers in oncology · 2025Article
- Article
- Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review.APL bioengineering · 2024Review
- Deep learning method for detecting fluorescence spots in cancer diagnostics via fluorescence in situ hybridization.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Many studies have recently used several deep learning methods for detecting skin cancer. However, hyperspectral imaging (HSI) is a noninvasive optics system that can obtain wavelength information on the location of skin cancer lesions and requires further investigation. Hyperspectral technology can capture hundreds of narrow bands of the electromagnetic spectrum both within and outside the visible wavelength range as well as bands that enhance the distinction of image features. The dataset from the ISIC library was used in this study to detect and classify skin cancer on the basis of basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and seborrheic keratosis (SK). The dataset was divided into training and test sets, and you only look once (YOLO) version 5 was applied to train the model. The model performance was judged according to the generated confusion matrix and five indicating parameters, including precision, recall, specificity, accuracy, and the F1-score of the trained model. Two models, namely, hyperspectral narrowband image (HSI-NBI) and RGB classification, were built and then compared in this study to understand the performance of HSI with the RGB model. Experimental results showed that the HSI model can learn the SCC feature better than the original RGB image because the feature is more prominent or the model is not captured in other categories. The recall rate of the RGB and HSI models were 0.722 to 0.794, respectively, thereby indicating an overall increase of 7.5% when using the HSI model.
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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.