Precision Imaging for Early Detection of Esophageal Cancer
Early detection of early-stage esophageal cancer (ECA) is crucial for timely intervention and improved treatment outcomes. Hyperspectral imaging (HSI) and artificial intelligence (AI) technologies offer promising avenues for enhancing diagnostic accuracy in this context. This study utilized a datase...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/1/90 |
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| author | Po-Chun Yang Chien-Wei Huang Riya Karmakar Arvind Mukundan Tsung-Hsien Chen Chu-Kuang Chou Kai-Yao Yang Hsiang-Chen Wang |
| author_facet | Po-Chun Yang Chien-Wei Huang Riya Karmakar Arvind Mukundan Tsung-Hsien Chen Chu-Kuang Chou Kai-Yao Yang Hsiang-Chen Wang |
| author_sort | Po-Chun Yang |
| collection | DOAJ |
| description | Early detection of early-stage esophageal cancer (ECA) is crucial for timely intervention and improved treatment outcomes. Hyperspectral imaging (HSI) and artificial intelligence (AI) technologies offer promising avenues for enhancing diagnostic accuracy in this context. This study utilized a dataset comprising 3984 white light images (WLIs) and 3666 narrow-band images (NBIs). We employed the Yolov5 model, a state-of-the-art object detection algorithm, to predict early ECA based on the provided images. The dataset was divided into two subsets: RGB-WLIs and NBIs, and four distinct models were trained using these datasets. The experimental results revealed that the prediction performance of the training model was notably enhanced when using HSI compared to general NBI training. The HSI training model demonstrated an 8% improvement in accuracy, along with a 5–8% enhancement in precision and recall measures. Notably, the model trained with WLIs exhibited the most significant improvement. Integration of HSI with AI technologies improves the prediction performance for early ECA detection. This study underscores the potential of deep learning identification models to aid in medical detection research. Integrating these models with endoscopic diagnostic systems in healthcare settings could offer faster and more accurate results, thereby improving overall detection performance. |
| format | Article |
| id | doaj-art-5a7ea9df60124d42943d4c505475a4bd |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-5a7ea9df60124d42943d4c505475a4bd2025-01-24T13:23:14ZengMDPI AGBioengineering2306-53542025-01-011219010.3390/bioengineering12010090Precision Imaging for Early Detection of Esophageal CancerPo-Chun Yang0Chien-Wei Huang1Riya Karmakar2Arvind Mukundan3Tsung-Hsien Chen4Chu-Kuang Chou5Kai-Yao Yang6Hsiang-Chen Wang7Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 60002, TaiwanDepartment of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Lingya District, Kaohsiung City 80284, TaiwanDepartment of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi 62102, TaiwanDepartment of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi 62102, TaiwanDepartment of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 60002, TaiwanDivision of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 60002, TaiwanDepartment of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Lingya District, Kaohsiung City 80284, TaiwanDepartment of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi 62102, TaiwanEarly detection of early-stage esophageal cancer (ECA) is crucial for timely intervention and improved treatment outcomes. Hyperspectral imaging (HSI) and artificial intelligence (AI) technologies offer promising avenues for enhancing diagnostic accuracy in this context. This study utilized a dataset comprising 3984 white light images (WLIs) and 3666 narrow-band images (NBIs). We employed the Yolov5 model, a state-of-the-art object detection algorithm, to predict early ECA based on the provided images. The dataset was divided into two subsets: RGB-WLIs and NBIs, and four distinct models were trained using these datasets. The experimental results revealed that the prediction performance of the training model was notably enhanced when using HSI compared to general NBI training. The HSI training model demonstrated an 8% improvement in accuracy, along with a 5–8% enhancement in precision and recall measures. Notably, the model trained with WLIs exhibited the most significant improvement. Integration of HSI with AI technologies improves the prediction performance for early ECA detection. This study underscores the potential of deep learning identification models to aid in medical detection research. Integrating these models with endoscopic diagnostic systems in healthcare settings could offer faster and more accurate results, thereby improving overall detection performance.https://www.mdpi.com/2306-5354/12/1/90esophageal cancerhyperspectral imagingobject recognitionYOLOv5squamous esophageal carcinoma |
| spellingShingle | Po-Chun Yang Chien-Wei Huang Riya Karmakar Arvind Mukundan Tsung-Hsien Chen Chu-Kuang Chou Kai-Yao Yang Hsiang-Chen Wang Precision Imaging for Early Detection of Esophageal Cancer Bioengineering esophageal cancer hyperspectral imaging object recognition YOLOv5 squamous esophageal carcinoma |
| title | Precision Imaging for Early Detection of Esophageal Cancer |
| title_full | Precision Imaging for Early Detection of Esophageal Cancer |
| title_fullStr | Precision Imaging for Early Detection of Esophageal Cancer |
| title_full_unstemmed | Precision Imaging for Early Detection of Esophageal Cancer |
| title_short | Precision Imaging for Early Detection of Esophageal Cancer |
| title_sort | precision imaging for early detection of esophageal cancer |
| topic | esophageal cancer hyperspectral imaging object recognition YOLOv5 squamous esophageal carcinoma |
| url | https://www.mdpi.com/2306-5354/12/1/90 |
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