Medical physics · 2023
HCEs-Net: Hepatic cystic echinococcosis classification ensemble model based on tree-structured Parzen estimator and snap-shot approach.
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Wang Zhengye, Kuerban Kadiliya, Zhou Zihang, Hailati Miwueryiti, Aihematiniyazi Renaguli, Wang Xiaorong, Yan Chuanbo
Аннотация
BACKGROUND: Hepatic cystic echinococcosis (HCE) still has a high misdiagnosis rate, and misdiagnosis may lead to wrong treatments seriously harmful for the patients. Precise diagnosis of HCE relies heavily on the experience of clinical experts with auxiliary diagnostic tools using medical images.
PURPOSE: This paper intends to improve the diagnostic accuracy for HCE by employing a method which combines deep learning with ensemble method.
METHODS: We proposed a method, namely HCEs-Net, for classification of five HCE subtypes using ultrasound images. It takes first the snap-shot strategy to obtain sub-models from the pre-trained VGG19, ResNet18, ViT-Base, and ConvNeXt-T models, then a stacking process to ensemble those sub-models. Afterwards, it uses the tree-structured Pazren estimator (TPE) to optimize the hyperparameters. The experiments were evaluated by the five-fold cross-validation process.
RESULTS: A total of 3083 abdominal ultrasound images from 972 patients covering five subtypes of HCE were utilized in this study. The experiments were conducted to predict the HCE subtype, and results of modeling performance evaluation were reported in terms of precision, recall, F1-score, and AUC. The stacking model based on three ConvNeXt-T sub-models showed the best performance, with precision 85.9%, recall 85.5%, F1-score 85.7%, and AUC 0.971 which are higher than the compared state-of-the-art models.
CONCLUSION: The stacking model of three ConvNeXt-T sub-models shows comparable or superior performance to the other methods, including VGG19, ResNet18 and ViT-Base. It has the potential to enhance clinical diagnosis for HCE.
PURPOSE: This paper intends to improve the diagnostic accuracy for HCE by employing a method which combines deep learning with ensemble method.
METHODS: We proposed a method, namely HCEs-Net, for classification of five HCE subtypes using ultrasound images. It takes first the snap-shot strategy to obtain sub-models from the pre-trained VGG19, ResNet18, ViT-Base, and ConvNeXt-T models, then a stacking process to ensemble those sub-models. Afterwards, it uses the tree-structured Pazren estimator (TPE) to optimize the hyperparameters. The experiments were evaluated by the five-fold cross-validation process.
RESULTS: A total of 3083 abdominal ultrasound images from 972 patients covering five subtypes of HCE were utilized in this study. The experiments were conducted to predict the HCE subtype, and results of modeling performance evaluation were reported in terms of precision, recall, F1-score, and AUC. The stacking model based on three ConvNeXt-T sub-models showed the best performance, with precision 85.9%, recall 85.5%, F1-score 85.7%, and AUC 0.971 which are higher than the compared state-of-the-art models.
CONCLUSION: The stacking model of three ConvNeXt-T sub-models shows comparable or superior performance to the other methods, including VGG19, ResNet18 and ViT-Base. It has the potential to enhance clinical diagnosis for HCE.