Deep Learning Model for Automated Cutaneous Squamous Cell Carcinoma Grading
Accurate and efficient grading of cutaneous squamous cell carcinoma (cSCC) is critical for effective treatment and prognosis, but traditional manual grading methods are subjective and time-consuming. This study aimed to develop and validate a deep learning (DL) model for automated cSCC grading, pot...
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Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca
2025-05-01
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| Series: | Applied Medical Informatics |
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| Online Access: | https://ami.info.umfcluj.ro/index.php/AMI/article/view/1180 |
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| author | Alexandra BURUIANĂ Mircea-Sebastian ŞERBĂNESCU Bogdan-Alexandru GHEBAN |
| author_facet | Alexandra BURUIANĂ Mircea-Sebastian ŞERBĂNESCU Bogdan-Alexandru GHEBAN |
| author_sort | Alexandra BURUIANĂ |
| collection | DOAJ |
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Accurate and efficient grading of cutaneous squamous cell carcinoma (cSCC) is critical for effective treatment and prognosis, but traditional manual grading methods are subjective and time-consuming. This study aimed to develop and validate a deep learning (DL) model for automated cSCC grading, potentially improving diagnostic accuracy and efficiency. Three different deep neural network (DNN) architectures (AlexNet, GoogLeNet, and ResNet-18) were trained using transfer learning on a dataset of 300 histopathological images of cSCC. The performance of the models was evaluated based on accuracy, sensitivity, specificity, and area under the curve (AUC). A clinical validation was conducted on 60 images, comparing the DNNs' predictions with those made by a panel of pathologists. The DL models achieved high performance metrics (accuracy >85%, sensitivity >85%, specificity >92%, AUC >97%), demonstrating their potential for objective and efficient cSCC grading. The strong agreement observed between the DNNs and the panel of pathologists, as well as the consistency across different network architectures, further supports the reliability and accuracy of the DL models. The top-performing models have been made publicly available to facilitate further research and potential clinical implementation. This study highlights the promising role of DL in enhancing cSCC diagnosis and, ultimately, improving patient care.
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| format | Article |
| id | doaj-art-a9b1fec44c174a429f9d2f0cf82f0bbe |
| institution | DOAJ |
| issn | 2067-7855 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca |
| record_format | Article |
| series | Applied Medical Informatics |
| spelling | doaj-art-a9b1fec44c174a429f9d2f0cf82f0bbe2025-08-20T02:39:54ZengIuliu Hatieganu University of Medicine and Pharmacy, Cluj-NapocaApplied Medical Informatics2067-78552025-05-0147Suppl. 1Deep Learning Model for Automated Cutaneous Squamous Cell Carcinoma GradingAlexandra BURUIANĂ0Mircea-Sebastian ŞERBĂNESCU1Bogdan-Alexandru GHEBAN2Department of Pathology, Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, Petru Rareş Str., no. 2, 200349 Craiova, RomaniaDepartment 1, Faculty of Medical Assistance and Health Sciences, “Iuliu Haţieganu” University of Medicine and Pharmacy, Victor Babeş Str., no. 8, 400012 Cluj-Napoca, Romania Accurate and efficient grading of cutaneous squamous cell carcinoma (cSCC) is critical for effective treatment and prognosis, but traditional manual grading methods are subjective and time-consuming. This study aimed to develop and validate a deep learning (DL) model for automated cSCC grading, potentially improving diagnostic accuracy and efficiency. Three different deep neural network (DNN) architectures (AlexNet, GoogLeNet, and ResNet-18) were trained using transfer learning on a dataset of 300 histopathological images of cSCC. The performance of the models was evaluated based on accuracy, sensitivity, specificity, and area under the curve (AUC). A clinical validation was conducted on 60 images, comparing the DNNs' predictions with those made by a panel of pathologists. The DL models achieved high performance metrics (accuracy >85%, sensitivity >85%, specificity >92%, AUC >97%), demonstrating their potential for objective and efficient cSCC grading. The strong agreement observed between the DNNs and the panel of pathologists, as well as the consistency across different network architectures, further supports the reliability and accuracy of the DL models. The top-performing models have been made publicly available to facilitate further research and potential clinical implementation. This study highlights the promising role of DL in enhancing cSCC diagnosis and, ultimately, improving patient care. https://ami.info.umfcluj.ro/index.php/AMI/article/view/1180Cutaneous Squamous Cell CarcinomaDeep LearningHistological GradingTransfer Learning; Artificial Intelligence |
| spellingShingle | Alexandra BURUIANĂ Mircea-Sebastian ŞERBĂNESCU Bogdan-Alexandru GHEBAN Deep Learning Model for Automated Cutaneous Squamous Cell Carcinoma Grading Applied Medical Informatics Cutaneous Squamous Cell Carcinoma Deep Learning Histological Grading Transfer Learning; Artificial Intelligence |
| title | Deep Learning Model for Automated Cutaneous Squamous Cell Carcinoma Grading |
| title_full | Deep Learning Model for Automated Cutaneous Squamous Cell Carcinoma Grading |
| title_fullStr | Deep Learning Model for Automated Cutaneous Squamous Cell Carcinoma Grading |
| title_full_unstemmed | Deep Learning Model for Automated Cutaneous Squamous Cell Carcinoma Grading |
| title_short | Deep Learning Model for Automated Cutaneous Squamous Cell Carcinoma Grading |
| title_sort | deep learning model for automated cutaneous squamous cell carcinoma grading |
| topic | Cutaneous Squamous Cell Carcinoma Deep Learning Histological Grading Transfer Learning; Artificial Intelligence |
| url | https://ami.info.umfcluj.ro/index.php/AMI/article/view/1180 |
| work_keys_str_mv | AT alexandraburuiana deeplearningmodelforautomatedcutaneoussquamouscellcarcinomagrading AT mirceasebastianserbanescu deeplearningmodelforautomatedcutaneoussquamouscellcarcinomagrading AT bogdanalexandrugheban deeplearningmodelforautomatedcutaneoussquamouscellcarcinomagrading |