Application of deep learning to the classification of images from colposcopy
Autor: | Koji Horie, Yuichiro Miyamoto, Aki Hara, Kazuko Kurihara, Kensuke Tomio, Harushige Yokota, Masakazu Sato |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
0301 basic medicine
Cancer Research medicine.medical_specialty cervical cancer cervical intraepithelial neoplasia Cervical intraepithelial neoplasia 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine medicine Colposcopy Cervical cancer Artificial neural network medicine.diagnostic_test business.industry Carcinoma in situ Deep learning Medical record colposcopy deep learning Articles Severe dysplasia medicine.disease artificial intelligence 030104 developmental biology Oncology Radiology Artificial intelligence business |
Zdroj: | Oncology Letters |
ISSN: | 1792-1082 1792-1074 |
Popis: | The objective of the present study was to investigate whether deep learning could be applied successfully to the classification of images from colposcopy. For this purpose, a total of 158 patients who underwent conization were enrolled, and medical records and data from the gynecological oncology database were retrospectively reviewed. Deep learning was performed with the Keras neural network and TensorFlow libraries. Using preoperative images from colposcopy as the input data and deep learning technology, the patients were classified into three groups [severe dysplasia, carcinoma in situ (CIS) and invasive cancer (IC)]. A total of 485 images were obtained for the analysis, of which 142 images were of severe dysplasia (2.9 images/patient), 257 were of CIS (3.3 images/patient), and 86 were of IC (4.1 images/patient). Of these, 233 images were captured with a green filter, and the remaining 252 were captured without a green filter. Following the application of L2 regularization, L1 regularization, dropout and data augmentation, the accuracy of the validation dataset was ~50%. Although the present study is preliminary, the results indicated that deep learning may be applied to classify colposcopy images. |
Databáze: | OpenAIRE |
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