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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1423405
This article is part of the Research Topic Recent Trends and Advancements in Multispectral and Hyperspectral Imaging for Cancer Detection View all articles

Computer-Aided Endoscopic Diagnostic System Modified with Hyperspectral Imaging for the Classification of Esophageal Neoplasms

Provisionally accepted
Yao-Kuang Wang Yao-Kuang Wang 1Riya Karmakar Riya Karmakar 2Arvind Mukundan Arvind Mukundan 2Ting-Chun Men Ting-Chun Men 2Yu-Ming Tsao Yu-Ming Tsao 2Song-Cun Lu Song-Cun Lu 2I-Chen Wu I-Chen Wu 1Hsiang-Chen Wang Hsiang-Chen Wang 2*
  • 1 Kaohsiung Medical University, Kaohsiung, Taiwan
  • 2 National Chung Cheng University, Minxiong, Taiwan

The final, formatted version of the article will be published soon.

    The early detection of esophageal cancer is crucial to enhancing patient survival rates, and endoscopy remains the gold standard for identifying esophageal neoplasms. Despite this fact, accurately diagnosing superficial esophageal neoplasms poses a challenge, even for seasoned endoscopists. Recent advancements in computer-aided diagnostic systems, empowered by artificial intelligence (AI), have shown promising results in elevating the diagnostic precision for early-stage esophageal cancer. In this study, we expanded upon traditional red-green-blue (RGB) imaging by integrating the YOLO neural network algorithm with hyperspectral imaging (HSI) to evaluate the diagnostic efficacy of this innovative AI system for superficial esophageal neoplasms. A total of 1836 endoscopic images were utilized for model training, which included 858 white-light imaging (WLI) and 978 narrow-band imaging (NBI) samples. These images were categorized into three groups, namely, normal esophagus, esophageal squamous dysplasia, and esophageal squamous cell carcinoma (SCC). An additional set comprising 257 WLI and 267 NBI images served as the validation dataset to assess diagnostic accuracy. Within the RGB dataset, the diagnostic accuracies of the WLI and NBI systems for classifying images into normal, dysplasia, and SCC categories were 0.83 and 0.82, respectively. Conversely, the HSI dataset yielded higher diagnostic accuracies for the WLI and NBI systems, with scores of 0.90 and 0.89, respectively. The HSI dataset outperformed the RGB dataset, demonstrating an overall diagnostic accuracy improvement of 8%. Our findings underscored the advantageous impact of incorporating the HSI dataset in model training. Furthermore, the application of HSI in AI-driven image recognition algorithms significantly enhanced the diagnostic accuracy for early esophageal cancer

    Keywords: esophageal cancer, hyperspectral imaging, SAVE, Dysplasia, SSD, YOLOv5, YOLOv8, Narrow-band imaging

    Received: 27 Apr 2024; Accepted: 04 Nov 2024.

    Copyright: © 2024 Wang, Karmakar, Mukundan, Men, Tsao, Lu, Wu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Hsiang-Chen Wang, National Chung Cheng University, Minxiong, Taiwan

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