Optimizing Esophageal Cancer Diagnosis with Computer-Aided Detection by YOLO Models Combined with Hyperspectral Imaging

Weng, Wei-Chun; Huang, Chien-Wei; Su, Chang-Chao; Mukundan, Arvind; Karmakar, Riya; Chen, Tsung-Hsien; Avhad, Amey Rajesh; Chou, Chu-Kuang; Wang, Hsiang-Chen

doi:10.3390/diagnostics15131686

Open AccessArticle

Optimizing Esophageal Cancer Diagnosis with Computer-Aided Detection by YOLO Models Combined with Hyperspectral Imaging

by

Wei-Chun Weng

¹,

Chien-Wei Huang

^1,2,

Chang-Chao Su

³,

Arvind Mukundan

⁴

,

Riya Karmakar

⁴,

Tsung-Hsien Chen

⁵

,

Amey Rajesh Avhad

⁶,

Chu-Kuang Chou

^3,7,*

and

Hsiang-Chen Wang

^4,8,9,*

¹

Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Lingya District, Kaohsiung City 80284, Taiwan

²

Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, Pingtung County 90741, Taiwan

³

Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 60002, Taiwan

⁴

Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi 62102, Taiwan

⁵

Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi 60002, Taiwan

⁶

Department of Computer Science, Sanjivani College of Engineering, Station Rd., Singapur, Kopargaon 423603, Maharashtra, India

⁷

Obesity Center, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 60002, Taiwan

⁸

Department of Medical Research, Buddhist Tzu Chi Medical Foundation, Dalin Tzu Chi Hospital, No. 2, Minsheng Road, Dalin, Chiayi 62247, Taiwan

⁹

Hitspectra Intelligent Technology Co., Ltd., Kaohsiung 80661, Taiwan

^*

Authors to whom correspondence should be addressed.

Diagnostics 2025, 15(13), 1686; https://doi.org/10.3390/diagnostics15131686

Submission received: 13 May 2025 / Revised: 24 June 2025 / Accepted: 25 June 2025 / Published: 2 July 2025

(This article belongs to the Special Issue New Advances in Hyperspectral and Multispectral Imaging for Diseases Diagnosis)

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Abstract

Objective: Esophageal cancer (EC) is difficult to visually identify, rendering early detection crucial to avert the advancement and decline of the patient’s health. Methodology: This work aimed to acquire spectral information from EC images via Spectrum-Aided Visual Enhancer (SAVE) technology, which improves imaging beyond the limitations of conventional White-Light Imaging (WLI). The hyperspectral data acquired using SAVE were examined utilizing sophisticated deep learning methodologies, incorporating models such as YOLOv8, YOLOv7, YOLOv6, YOLOv5, Scaled YOLOv4, and YOLOv3. The models were assessed to create a reliable detection framework for accurately identifying the stage and location of malignant lesions. Results: The comparative examination of these models demonstrated that the SAVE method regularly surpassed WLI for specificity, sensitivity, and overall diagnostic efficacy. Significantly, SAVE improved precision and F1 scores for the majority of the models, which are essential measures for enhancing patient care and customizing effective medicines. Among the evaluated models, YOLOv8 showed exceptional performance. YOLOv8 demonstrated increased sensitivity to squamous cell carcinomas (SCCs), but YOLOv5 provided reliable outcomes across many situations, underscoring its adaptability. Conclusions: These findings highlight the clinical importance of combining SAVE technology with deep learning models for esophageal cancer screening. The enhanced diagnostic accuracy provided by SAVE, especially when integrated with CAD models, offers potential for improving early detection, precise diagnosis, and tailored treatment approaches in clinically pertinent scenarios.

Keywords: esophageal cancer; hyperspectral imaging; SAVE; dysplasia; SCC YOLOv5; YOLOv8; narrow-band imaging; white-light imaging

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MDPI and ACS Style

Weng, W.-C.; Huang, C.-W.; Su, C.-C.; Mukundan, A.; Karmakar, R.; Chen, T.-H.; Avhad, A.R.; Chou, C.-K.; Wang, H.-C. Optimizing Esophageal Cancer Diagnosis with Computer-Aided Detection by YOLO Models Combined with Hyperspectral Imaging. Diagnostics 2025, 15, 1686. https://doi.org/10.3390/diagnostics15131686

AMA Style

Weng W-C, Huang C-W, Su C-C, Mukundan A, Karmakar R, Chen T-H, Avhad AR, Chou C-K, Wang H-C. Optimizing Esophageal Cancer Diagnosis with Computer-Aided Detection by YOLO Models Combined with Hyperspectral Imaging. Diagnostics. 2025; 15(13):1686. https://doi.org/10.3390/diagnostics15131686

Chicago/Turabian Style

Weng, Wei-Chun, Chien-Wei Huang, Chang-Chao Su, Arvind Mukundan, Riya Karmakar, Tsung-Hsien Chen, Amey Rajesh Avhad, Chu-Kuang Chou, and Hsiang-Chen Wang. 2025. "Optimizing Esophageal Cancer Diagnosis with Computer-Aided Detection by YOLO Models Combined with Hyperspectral Imaging" Diagnostics 15, no. 13: 1686. https://doi.org/10.3390/diagnostics15131686

APA Style

Weng, W.-C., Huang, C.-W., Su, C.-C., Mukundan, A., Karmakar, R., Chen, T.-H., Avhad, A. R., Chou, C.-K., & Wang, H.-C. (2025). Optimizing Esophageal Cancer Diagnosis with Computer-Aided Detection by YOLO Models Combined with Hyperspectral Imaging. Diagnostics, 15(13), 1686. https://doi.org/10.3390/diagnostics15131686

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Optimizing Esophageal Cancer Diagnosis with Computer-Aided Detection by YOLO Models Combined with Hyperspectral Imaging

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