ArticleJournal of healthcare engineering2020
Assistant Diagnosis of Basal Cell Carcinoma and Seborrheic Keratosis in Chinese Population Using Convolutional Neural Network.
Article in Journal of healthcare engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 4 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 4 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Diagnostic accuracy of artificial intelligence compared to family physicians and dermatologists for skin conditions: a systematic review and meta-analysis.BMC primary care · 2025Pooled it
- Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.Frontiers in digital health · 2025Pooled it
- The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Artificial Intelligence for the Classification of Pigmented Skin Lesions in Populations with Skin of Color: A Systematic Review.Dermatology (Basel, Switzerland) · 2023Pooled it
- Global trends and academic landscapes of AI applications in basal cell carcinoma research: a bibliometric analysis.Frontiers in oncology · 2026Review
- The Use of Artificial Intelligence for Skin Cancer Detection in Asia-A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning in assisting dermatologists in classifying basal cell carcinoma from seborrheic keratosis.Frontiers in oncology · 2025Article
- A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis.NPJ digital medicine · 2024Review
- Transfer learning for medical image classification: a literature review.BMC medical imaging · 2022Review
- Basic Pan-Cancer Analysis of the Carcinogenic Effects of Cyclin-Dependent Kinase 4 (CDK4) in Human Surface Tumors.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
16 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: To evaluate CNN models' performance of identifying the clinical images of basal cell carcinoma (BCC) and seborrheic keratosis (SK) and to compare their performance with that of dermatologists. Methods: We constructed a Chinese skin diseases dataset which includes 1456 BCC and 1843 SK clinical images and the corresponding medical history. We evaluated the performance using four mainstream CNN structures and transfer learning techniques. We explored the interpretability of the CNN model and compared its performance with that of 21 dermatologists. Results: The fine-tuned InceptionResNetV2 achieved the best performance, with an accuracy and area under the curve of 0.855 and 0.919, respectively. Further experimental results suggested that the CNN model was not only interpretable but also had a performance comparable to that of dermatologists. Conclusions: This study is the first on the assistant diagnosis of BCC and SK based on the proposed dataset. The promising results suggested that CNN model's performance was comparable to that of expert dermatologists.
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