Diagnosis of Salivary Gland Tumors Using Transfer Learning with Fine-Tuning and Gradual Unfreezing

被引:1
|
作者
Cheng, Ping-Chia [1 ,2 ,3 ]
Chiang, Hui-Hua Kenny [1 ]
机构
[1] Natl Yang Ming Chiao Tung Univ, Dept Biomed Engn, Taipei 11221, Taiwan
[2] Far Eastern Mem Hosp, Dept Otolaryngol Head & Neck Surg, New Taipei 22060, Taiwan
[3] Asia Eastern Univ Sci & Technol, Dept Commun Engn, New Taipei 22060, Taiwan
关键词
salivary gland tumor; ultrasound; deep learning; convolutional neural network; transfer learning; gradient-weighted class activation mapping (Grad-CAM); CONVOLUTIONAL NEURAL-NETWORKS; CLASSIFICATION; ACCURACY;
D O I
10.3390/diagnostics13213333
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Ultrasound is the primary tool for evaluating salivary gland tumors (SGTs); however, tumor diagnosis currently relies on subjective features. This study aimed to establish an objective ultrasound diagnostic method using deep learning. We collected 446 benign and 223 malignant SGT ultrasound images in the training/validation set and 119 benign and 44 malignant SGT ultrasound images in the testing set. We trained convolutional neural network (CNN) models from scratch and employed transfer learning (TL) with fine-tuning and gradual unfreezing to classify malignant and benign SGTs. The diagnostic performances of these models were compared. By utilizing the pretrained ResNet50V2 with fine-tuning and gradual unfreezing, we achieved a 5-fold average validation accuracy of 0.920. The diagnostic performance on the testing set demonstrated an accuracy of 89.0%, a sensitivity of 81.8%, a specificity of 91.6%, a positive predictive value of 78.3%, and a negative predictive value of 93.2%. This performance surpasses that of other models in our study. The corresponding Grad-CAM visualizations were also presented to provide explanations for the diagnosis. This study presents an effective and objective ultrasound method for distinguishing between malignant and benign SGTs, which could assist in preoperative evaluation.
引用
收藏
页数:13
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