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
相关论文
共 50 条
  • [21] Fine-tuning transformers: Vocabulary transfer
    Mosin, Vladislav
    Samenko, Igor
    Kozlovskii, Borislav
    Tikhonov, Alexey
    Yamshchikov, Ivan P.
    ARTIFICIAL INTELLIGENCE, 2023, 317
  • [22] Diagnosis of Salivary Gland Tumors Using Ultrasound Radiomics
    Cheng, Ping-Chia
    Lo, Wu-Chia
    Liao, Li-Jen
    Chiang, Huihua Kenny
    ULTRASOUND IN MEDICINE AND BIOLOGY, 2025, 51 (05): : 815 - 822
  • [23] Fine-Tuning and Efficient VGG16 Transfer Learning Fault Diagnosis Method for Rolling Bearing
    Su, Jinglei
    Wang, Hongjun
    PROCEEDINGS OF INCOME-VI AND TEPEN 2021: PERFORMANCE ENGINEERING AND MAINTENANCE ENGINEERING, 2023, 117 : 453 - 461
  • [24] FINE-TUNING TRANSFER LEARNING MODEL IN WOVEN FABRIC PATTERN CLASSIFICATION
    Noprisson H.
    Ermatita E.
    Abdiansah A.
    Ayumi V.
    Purba M.
    Setiawan H.
    International Journal of Innovative Computing, Information and Control, 2022, 18 (06): : 1885 - 1894
  • [25] Comparison of fine-tuning strategies for transfer learning in medical image classification
    Davila, Ana
    Colan, Jacinto
    Hasegawa, Yasuhisa
    IMAGE AND VISION COMPUTING, 2024, 146
  • [26] A selective model for transfer learning in CNNs: optimization of fine-tuning layers
    Mallouk, Otmane
    Joudar, Nour-Eddine
    Ettaouil, Mohamed
    INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, 2024,
  • [27] Approaches of transfer learning and fine-tuning on the effects of performance of vehicle classification
    Huang, Deng-Yuan
    Lin, Wen-Lung
    Wang, Yu-Yun
    Journal of Computers (Taiwan), 2020, 31 (06): : 24 - 37
  • [28] A Deep Transfer Learning Approach to Fine-Tuning Facial Recognition Models
    Luttrell, Joseph
    Zhou, Zhaoxian
    Zhang, Yuanyuan.
    Zhang, Chaoyang
    Gong, Ping
    Yang, Bei
    Li, Runzhi
    PROCEEDINGS OF THE 2018 13TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2018), 2018, : 2671 - 2676
  • [29] An Application of Transfer Learning: Fine-Tuning BERT for Spam Email Classification
    Bhopale, Amol P.
    Tiwari, Ashish
    MACHINE LEARNING AND BIG DATA ANALYTICS (PROCEEDINGS OF INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND BIG DATA ANALYTICS (ICMLBDA) 2021), 2022, 256 : 67 - 77
  • [30] Transfer Learning and Fine-Tuning for Facial Expression Recognition with Class Balancing
    Ruzicka, Josef
    Lara, Adrian
    2024 L LATIN AMERICAN COMPUTER CONFERENCE, CLEI 2024, 2024,