Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models
Abstract To overcome the limitation of a single classification model’s inability to simultaneously identify multiple lesion targets within periapical radiographs, This study proposes YoCNET (Yolov5 + ConvNeXt), a novel deep learning integrated model. YoCNET leverages the target detection capability...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-75748-9 |
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| author | Jian Liu Xiaohua Liu Yu Shao Yongzhen Gao Kexu Pan Chaoran Jin Honghai Ji Yi Du Xijiao Yu |
| author_facet | Jian Liu Xiaohua Liu Yu Shao Yongzhen Gao Kexu Pan Chaoran Jin Honghai Ji Yi Du Xijiao Yu |
| author_sort | Jian Liu |
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| description | Abstract To overcome the limitation of a single classification model’s inability to simultaneously identify multiple lesion targets within periapical radiographs, This study proposes YoCNET (Yolov5 + ConvNeXt), a novel deep learning integrated model. YoCNET leverages the target detection capability of Yolov5 and the image classification capability of ConvNeXt to achieve automatic segmentation of individual teeth and concurrent detection of periapical lesions across multiple teeth. A dataset of 1,305 periapical radiographs was used to train and validate the ConvNeXt and ResNet34 models, with an 8:2 split for training and validation. Deciduous teeth were excluded from the dataset. Furthermore, 717 individual teeth images were extracted from 200 previously unused periapical radiographs for integrated model validation. Evaluation metrics included accuracy, precision, sensitivity, F1 score, AUC (Area Under Curve), and a confusion matrix.The YoCNET integrated model demonstrated values of 90.93%, 98.88%, 85.30%, 0.9159, and 0.9757 for accuracy, precision, sensitivity, F1 score, and AUC, respectively. These metrics were superior to those achieved by the YoRNET (Yolov5 + ResNet34) integrated model, which recorded 80.47%, 83.78%, 82.16%, 0.8296, and 0.8822. The integrated model achieved high accuracy and efficiency in automatic teeh segmentation by Yolov5 and in automatically detecting multiple periapical lesions by ConvNeXt. YoCNET exhibited superior overall data performance, making it a more suitable deep learning integrated model for clinical applications. |
| format | Article |
| id | doaj-art-d861faf89aed49c4ace2fa01730d9923 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d861faf89aed49c4ace2fa01730d99232025-08-20T02:11:29ZengNature PortfolioScientific Reports2045-23222024-10-0114111010.1038/s41598-024-75748-9Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated modelsJian Liu0Xiaohua Liu1Yu Shao2Yongzhen Gao3Kexu Pan4Chaoran Jin5Honghai Ji6Yi Du7Xijiao Yu8School of Stomatology, Shandong Second Medical UniversityDepartment of Endodontics, Jinan Stamotological Hospital, Jinan Key Laboratory of Oral Tissue Regeneration, Shandong Provincial Health Commission Key Laboratory of Oral Diseases and Tissue RegenerationShandong Xintai Huizhi Health and Medical Big Data Co., Ltd.School of Stomatology, Binzhou Medical CollegeSchool of Stomatology, Shandong Second Medical UniversitySchool of Stomatology, Shandong Second Medical UniversitySchool of Stomatology, Shandong Second Medical UniversityDepartment of Endodontics, Central Laboratory, Jinan Stamotological Hospital, Jinan Key Laboratory of Oral Tissue Regeneration, Shandong Provincial Health Commission Key Laboratory of Oral Diseases and Tissue RegenerationSchool of Stomatology, Shandong Second Medical UniversityAbstract To overcome the limitation of a single classification model’s inability to simultaneously identify multiple lesion targets within periapical radiographs, This study proposes YoCNET (Yolov5 + ConvNeXt), a novel deep learning integrated model. YoCNET leverages the target detection capability of Yolov5 and the image classification capability of ConvNeXt to achieve automatic segmentation of individual teeth and concurrent detection of periapical lesions across multiple teeth. A dataset of 1,305 periapical radiographs was used to train and validate the ConvNeXt and ResNet34 models, with an 8:2 split for training and validation. Deciduous teeth were excluded from the dataset. Furthermore, 717 individual teeth images were extracted from 200 previously unused periapical radiographs for integrated model validation. Evaluation metrics included accuracy, precision, sensitivity, F1 score, AUC (Area Under Curve), and a confusion matrix.The YoCNET integrated model demonstrated values of 90.93%, 98.88%, 85.30%, 0.9159, and 0.9757 for accuracy, precision, sensitivity, F1 score, and AUC, respectively. These metrics were superior to those achieved by the YoRNET (Yolov5 + ResNet34) integrated model, which recorded 80.47%, 83.78%, 82.16%, 0.8296, and 0.8822. The integrated model achieved high accuracy and efficiency in automatic teeh segmentation by Yolov5 and in automatically detecting multiple periapical lesions by ConvNeXt. YoCNET exhibited superior overall data performance, making it a more suitable deep learning integrated model for clinical applications.https://doi.org/10.1038/s41598-024-75748-9 |
| spellingShingle | Jian Liu Xiaohua Liu Yu Shao Yongzhen Gao Kexu Pan Chaoran Jin Honghai Ji Yi Du Xijiao Yu Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models Scientific Reports |
| title | Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models |
| title_full | Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models |
| title_fullStr | Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models |
| title_full_unstemmed | Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models |
| title_short | Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models |
| title_sort | periapical lesion detection in periapical radiographs using the latest convolutional neural network convnext and its integrated models |
| url | https://doi.org/10.1038/s41598-024-75748-9 |
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