Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging

作者：Shih‐Tien Hsu, Yujie Su, Chian-Huei Hung, Ming-Jer Chen, Chien‐Hsing Lu, Chih‐En Kuo · 发表于：BMC Medical Informatics and Decision Making · 年份：2022 · DOI：10.1186/s12911-022-02047-6 · 被引用次数：63 · 研究领域：Ovarian cancer diagnosis and treatment、AI in cancer detection、Ovarian function and disorders

BACKGROUND: Upon the discovery of ovarian cysts, obstetricians, gynecologists, and ultrasound examiners must address the common clinical challenge of distinguishing between benign and malignant ovarian tumors. Numerous types of ovarian tumors exist, many of which exhibit similar characteristics that increase the ambiguity in clinical diagnosis. Using deep learning technology, we aimed to develop a method that rapidly and accurately assists the different diagnosis of ovarian tumors in ultrasound images. METHODS: Based on deep learning method, we used ten well-known convolutional neural network models (e.g., Alexnet, GoogleNet, and ResNet) for training of transfer learning. To ensure method stability and robustness, we repeated the random sampling of the training and validation data ten times. The mean of the ten test results was set as the final assessment data. After the training process was completed, the three models with the highest ratio of calculation accuracy to time required for classification were used for ensemble learning pertaining. Finally, the interpretation results of the ensemble classifier were used as the final results. We also applied ensemble gradient-weighted class activation mapping (Grad-CAM) technology to visualize the decision-making results of the models. RESULTS: The highest mean accuracy, mean sensitivity, and mean specificity of ten single CNN models were 90.51 ± 4.36%, 89.77 ± 4.16%, and 92.00 ± 5.95%, respectively. The mean accuracy, mean sensiti...