ArticleRomanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie
Automated cutaneous squamous cell carcinoma grading using deep learning with transfer learning.
Article in Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Deepath-SCC: a deep learning model for accurate tissue origin identification in squamous cell carcinoma.NPJ precision oncology · 2026Article
- Enhanced metastasis risk prediction in cutaneous squamous cell carcinoma using deep learning and computational histopathology.NPJ precision oncology · 2025Article
- Nuclear morphology explained through digital morphometry: differentiating nuclear features across the three histological grades in cutaneous squamous cell carcinoma.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionHistological grading of cutaneous squamous cell carcinoma (cSCC) is crucial for prognosis and treatment decisions, but manual grading is subjective and time-consuming.
aimThis study aimed to develop and validate a deep learning (DL)-based model for automated cSCC grading, potentially improving diagnostic accuracy (ACC) and efficiency. MATERIALS AND
methodsThree deep neural networks (DNNs) with different architectures (AlexNet, GoogLeNet, ResNet-18) were trained using transfer learning on a dataset of 300 histopathological images of cSCC. The models were evaluated on their ACC, sensitivity (SN), specificity (SP), and area under the curve (AUC). Clinical validation was performed on 60 images, comparing the DNNs' predictions with those of a panel of pathologists.
resultsThe models achieved high performance metrics (ACC>85%, SN>85%, SP>92%, AUC>97%) demonstrating their potential for objective and efficient cSCC grading. The high agreement between the DNNs and pathologists, as well as among different network architectures, further supports the reliability and ACC of the DL models. The top-performing models are publicly available, facilitating further research and potential clinical implementation.
conclusionsThis study highlights the promising role of DL in enhancing cSCC diagnosis, ultimately improving patient care.
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