Deep Learning-Based Detection and Classification System for Salivary Gland Tumors Using Ultrasound Imaging.
Authors
Affiliations (6)
Affiliations (6)
- Department of Otolaryngology, Head and Neck Surgery, Far Eastern Memorial Hospital, New Taipei City 22060, Taiwan.
- Department of Communication Engineering, Asia Eastern University of Science and Technology, New Taipei City 22060, Taiwan.
- Head and Neck Cancer Surveillance and Research Study Group, Far Eastern Memorial Hospital, New Taipei City 22060, Taiwan.
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan.
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan 32003, Taiwan.
- Department of Electrical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan.
Abstract
<b>Objectives</b>: Ultrasound is the primary modality for salivary gland tumor (SGT) evaluation, yet its reliance on subjective interpretation can lead to diagnostic variance. This study aims to develop and validate a two-stage deep learning system to automate SGT detection and classification. <b>Methods</b>: The study compiled a dataset of ultrasound images from patients with pathologically confirmed SGTs across three sequential cohorts: a training set (687 images, 2007-2020), a validation set (78 images, 2021), and a testing set (101 images, 2022). A YOLOv8 model was trained for tumor detection, and a modified ResNet50V2 model was utilized for benign versus malignant classification. The resulting two-stage pipeline was deployed on a local desktop system and further evaluated using two independent datasets: an internal validation set (56 images, 2023) and an external online dataset (57 images). <b>Results</b>: On the testing set, the YOLOv8 model achieved a bounding-box precision of 0.94 and a recall of 0.95 for tumor detection. When integrated with the classification model, the two-stage desktop system yielded an accuracy of 84%, sensitivity of 74%, and specificity of 87%. This system maintained comparable performance, demonstrating accuracies of 82% and 81%, sensitivities of 100% and 71%, and specificities of 81% and 86% on the internal and external validation sets, respectively. <b>Conclusions</b>: This study introduced a two-stage deep learning desktop system for automated SGT diagnosis. The edge-deployed system may serve as an objective adjunct to conventional ultrasound interpretation, potentially assisting clinicians during preoperative evaluation.