ArticleDiagnostics (Basel, Switzerland)2021
Automated Skin Lesion Classification on Ultrasound Images.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- [Deep learning-driven high-frequency ultrasound method for skin aging assessment].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging.Scientific reports · 2025Article
- Automatic Assessment of AK Stage Based on Dermatoscopic and HFUS Imaging-A Preliminary Study.Journal of clinical medicine · 2024Article
- The Role of Artificial Intelligence in Early Diagnosis and Molecular Classification of Head and Neck Skin Cancers: A Multidisciplinary Approach.Diagnostics (Basel, Switzerland) · 2024Review
- Article
- Skin Lesion Detection Using Hand-Crafted and DL-Based Features Fusion and LSTM.Diagnostics (Basel, Switzerland) · 2022Article
- Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review.Sensors (Basel, Switzerland) · 2022Review
- Imaging findings of malignant skin tumors: radiological-pathological correlation.Insights into imaging · 2022Review
- InSiNet: a deep convolutional approach to skin cancer detection and segmentation.Medical & biological engineering & computing · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The growing incidence of skin cancer makes computer-aided diagnosis tools for this group of diseases increasingly important. The use of ultrasound has the potential to complement information from optical dermoscopy. The current work presents a fully automatic classification framework utilizing fully-automated (FA) segmentation and compares it with classification using two semi-automated (SA) segmentation methods. Ultrasound recordings were taken from a total of 310 lesions (70 melanoma, 130 basal cell carcinoma and 110 benign nevi). A support vector machine (SVM) model was trained on 62 features, with ten-fold cross-validation. Six classification tasks were considered, namely all the possible permutations of one class versus one or two remaining classes. The receiver operating characteristic (
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Registered trials
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