Enhancing esophageal cancer detection using a deep learning framework and a novel spectrum-aided vision enhancer for virtual narrow band imaging.
Authors
Affiliations (15)
Affiliations (15)
- Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st.Rd., Lingya District, Kaohsiung City, 80284, Taiwan.
- Department of Medicine, National Defense Medical University, No.161, Sec. 6, Minquan E. Rd., Neihu District, Taipei City, 11490, Taiwan.
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, 62102, Chia Yi, Taiwan.
- Department of Trauma, Changhua Christian Hospital, Changhua, No. 135, Nanxiao St., Changhua City, 50006, Changhua County, Taiwan.
- Department of Biomedical Imaging, Chennai Institute of Technology, Sarathy Nagar, Chennai, 600069, Tamil Nadu, India.
- Department of Computer Science Engineering, School of Science and Technology, Sanjivani University, Kopargaon, India.
- Department of Integrated Bachelor of Technology, School of Science and Technology, Sanjivani University, Kopargaon, India.
- Department of Computer Science, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, No.42, Avadi-Vel Tech Road Vel Nagar, Avadi, Chennai, 600062, Tamil Nadu, India.
- School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5, Tiwanon Rd, Bangkadi, Meung, Bangkok, 12000, Patumthani, Thailand.
- Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st.Rd., Lingya District, Kaohsiung City, 80284, Taiwan. [email protected].
- Department of Medicine, National Defense Medical University, No.161, Sec. 6, Minquan E. Rd., Neihu District, Taipei City, 11490, Taiwan. [email protected].
- Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, 90741, Pingtung County, Taiwan. [email protected].
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, 62102, Chia Yi, Taiwan. [email protected].
- Department of Medical Research, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 2, Minsheng Road, Dalin, Chiayi, 62247, Taiwan. [email protected].
- Development of Technology, Hitspectra Intelligent Technology Co., Ltd., Kaohsiung, 80661, Taiwan. [email protected].
Abstract
Esophageal cancer is a highly aggressive malignancy where early detection is critical for survival. However, early-stage lesions typically present subtle mucosal changes that are difficult to identify using standard White Light Imaging (WLI), and hardware-based Narrow Band Imaging (NBI) is not universally available. In this study, we propose a novel image processing algorithm termed the Spectrum-Aided Vision Enhancer (SAVE) to address these challenges in computer-aided diagnosis (CAD). Leveraging hyperspectral data principles, SAVE transforms standard WLI endoscopic images into enhanced, NBI-like representations, significantly improving mucosal contrast and lesion visibility without requiring additional hardware. To validate the efficacy of this approach for medical image analysis, we utilized a dataset of Squamous Cell Carcinoma (SCC) and dysplasia. We conducted a comprehensive comparative analysis using five state-of-the-art deep learning models: YOLOv8, InceptionV3, Inception-ResNet-V2, ConvNeXt-V2, and MobileNetV2. Experimental results demonstrate that models trained on SAVE-enhanced images significantly outperform those trained on traditional WLI in both classification and detection tasks. This study presents a cost-effective, software-driven solution that integrates advanced image processing with deep learning, offering a robust tool for the automated screening of esophageal malignancies.