ArticleDiagnostics (Basel, Switzerland)2024
Assessing the Efficacy of the Spectrum-Aided Vision Enhancer (SAVE) to Detect Acral Lentiginous Melanoma, Melanoma In Situ, Nodular Melanoma, and Superficial Spreading Melanoma.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Artificial intelligence with deep convolutional neural network-based clinical decision-making on kidney Oncology using multimodal imaging.Scientific reports · 2026Article
- Clinical Application of Vision Transformers for Melanoma Classification: A Multi-Dataset Evaluation Study.Cancers · 2025Article
- Early-Stage Melanoma Benchmark Dataset.Cancers · 2025Article
- The Role of Oxidative Stress in Skin Disorders Associated with Alcohol Dependency and Antioxidant Therapies.Molecules (Basel, Switzerland) · 2025Review
- MST-AI: Skin Color Estimation in Skin Cancer Datasets.Journal of imaging · 2025Article
- Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Review of Non-Invasive Imaging Technologies for Cutaneous Melanoma.Biosensors · 2025Review
- Low Intratumoral CD200 Protein Expression in Primary Merkel Cell Carcinoma Is a Strong Predictor for Disease Relapse.Cancers · 2025Article
- Review
- 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
- Article
- Sine oculis homeobox homolog family function in gastrointestinal cancer: Progression and comprehensive analysis.World journal of clinical oncology · 2025Review
- AI-Driven Enhancement of Skin Cancer Diagnosis: A Two-Stage Voting Ensemble Approach Using Dermoscopic Data.Cancers · 2025Article
- EnsembleSkinNet: a transfer learning-based framework for efficient skin cancer detection with explainable AI integration.Frontiers in oncology · 2025Article
- Advances in intelligent recognition and diagnosis of skin scar images: concepts, methods, challenges, and future trends.Frontiers in medicine · 2025Review
- SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Blind Separation of Skin Chromophores from Multispectral Dermatological Images.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
Skin cancer is the predominant form of cancer worldwide, including 75% of all cancer cases. This study aims to evaluate the effectiveness of the spectrum-aided visual enhancer (SAVE) in detecting skin cancer. This paper presents the development of a novel algorithm for snapshot hyperspectral conversion, capable of converting RGB images into hyperspectral images (HSI). The integration of band selection with HSI has facilitated the identification of a set of narrow band images (NBI) from the RGB images. This study utilizes various iterations of the You Only Look Once (YOLO) machine learning (ML) framework to assess the precision, recall, and mean average precision in the detection of skin cancer. YOLO is commonly preferred in medical diagnostics due to its real-time processing speed and accuracy, which are essential for delivering effective and efficient patient care. The precision, recall, and mean average precision (mAP) of the SAVE images show a notable enhancement in comparison to the RGB images. This work has the potential to greatly enhance the efficiency of skin cancer detection, as well as improve early detection rates and diagnostic accuracy. Consequently, it may lead to a reduction in both morbidity and mortality rates.
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Registered trials
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