2022
DOI: 10.3389/fonc.2022.869421
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A Comparative Study of Multiple Deep Learning Models Based on Multi-Input Resolution for Breast Ultrasound Images

Abstract: PurposeThe purpose of this study was to explore the performance of different parameter combinations of deep learning (DL) models (Xception, DenseNet121, MobileNet, ResNet50 and EfficientNetB0) and input image resolutions (REZs) (224 × 224, 320 × 320 and 488 × 488 pixels) for breast cancer diagnosis.MethodsThis multicenter study retrospectively studied gray-scale ultrasound breast images enrolled from two Chinese hospitals. The data are divided into training, validation, internal testing and external testing se… Show more

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Cited by 3 publications
(2 citation statements)
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“…Recently, Wu et al compared different deep-learning models based on the multi-input resolution for breast ultrasound images. They attempted to select the best DL combination (28). This study established an AI model with selected preoperative clinical features to improve the accuracy of the assessment of benign and malignant breast lesions.…”
Section: Discussionmentioning
confidence: 99%
“…Recently, Wu et al compared different deep-learning models based on the multi-input resolution for breast ultrasound images. They attempted to select the best DL combination (28). This study established an AI model with selected preoperative clinical features to improve the accuracy of the assessment of benign and malignant breast lesions.…”
Section: Discussionmentioning
confidence: 99%
“…In our previous work, 44 we had completed training and testing four AI models (Xception, 45 MobileNet, 46 ResNet50, 47 and DenseNet121 48 ) with three input image resolutions (224 × 224, 320 × 320, and 448 × 448 pixels). According to the inclusion and exclusion criteria, 3447 females breast nodules were finally included.…”
Section: Methodsmentioning
confidence: 99%