Computer‐aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks

Moon, Woo Kyung; Lee, Yan Wei; Hao, Ke; Lee, Sang Ho; Huang, Chiun‐Sheng; Chang, Rong‐Yeu

doi:10.1016/j.cmpb.2020.105361

Cited by 196 publications

(137 citation statements)

References 37 publications

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“…In recent studies, researchers used different DLMs to diagnose breast tumors on ultrasound images, and the one that performed best was selected after comparison. The purpose of these studies was to develop only a DLM for the classification of malignant and benign masses (25)(26)(27)(28)(29). In our study, we sought to develop a DLM not only for classifying masses but also for reducing unnecessary biopsy.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model

Zhang

Wang

et al. 2021

Front. Oncol.

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Background: Breast ultrasound is the first choice for breast tumor diagnosis in China, but the Breast Imaging Reporting and Data System (BI-RADS) categorization routinely used in the clinic often leads to unnecessary biopsy. Radiologists have no ability to predict molecular subtypes with important pathological information that can guide clinical treatment.Materials and Methods: This retrospective study collected breast ultrasound images from two hospitals and formed training, test and external test sets after strict selection, which included 2,822, 707, and 210 ultrasound images, respectively. An optimized deep learning model (DLM) was constructed with the training set, and the performance was verified in both the test set and the external test set. Diagnostic results were compared with the BI-RADS categorization determined by radiologists. We divided breast cancer into different molecular subtypes according to hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression. The ability to predict molecular subtypes using the DLM was confirmed in the test set.Results: In the test set, with pathological results as the gold standard, the accuracy, sensitivity and specificity were 85.6, 98.7, and 63.1%, respectively, according to the BI-RADS categorization. The same set achieved an accuracy, sensitivity, and specificity of 89.7, 91.3, and 86.9%, respectively, when using the DLM. For the test set, the area under the curve (AUC) was 0.96. For the external test set, the AUC was 0.90. The diagnostic accuracy was 92.86% with the DLM in BI-RADS 4a patients. Approximately 70.76% of the cases were judged as benign tumors. Unnecessary biopsy was theoretically reduced by 67.86%. However, the false negative rate was 10.4%. A good prediction effect was shown for the molecular subtypes of breast cancer with the DLM. The AUC were 0.864, 0.811, and 0.837 for the triple-negative subtype, HER2 (+) subtype and HR (+) subtype predictions, respectively.Conclusion: This study showed that the DLM was highly accurate in recognizing breast tumors from ultrasound images. Thus, the DLM can greatly reduce the incidence of unnecessary biopsy, especially for patients with BI-RADS 4a. In addition, the predictive ability of this model for molecular subtypes was satisfactory,which has specific clinical application value.

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Section: Discussionmentioning

confidence: 99%

Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model

Zhang

Wang

et al. 2021

Front. Oncol.

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“…Recently, various models relied on deep learning that has been used in BUS image classification. For example, references [36,[38][39][40] used deep learning methods to extract BUS image features and present classification. Compared with the direct use of traditional deep learning models such as VGG16, VGG19, ResNet50, and Inception V3, they got better results.…”

Section: Discussionmentioning

confidence: 99%

Multi‐Features‐Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

Zhuang

Yang

Zhuang

et al. 2021

Computational Intelligence and Neuroscience

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Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.

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“…Then, prediction results were calculated by taking the simple arithmetic mean of CNN models' posterior output probabilities. Similarly, in [33][34][35], to increase the performance of the classification system on limited number of medical images, the transfer learning and ensemble methods were used together. To create ensembles, they took the advantages of pre-trained VGGNet, ResNet and DenseNet architectures.…”

Section: Related Workmentioning

confidence: 99%

A hybrid approach based on transfer and ensemble learning for improving performances of deep learning models on small datasets

Gültekin¹,

Uğur²

2021

Turk J Elec Eng & Comp Sci

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The need for high-volume data is one of the challenging requirements of the deep learning methods and it makes it harder to apply deep learning algorithms to domains in which the data sources are limited, in other words small. These domains may vary from medical diagnosis to satellite imaging. The performances of the deep learning methods on small datasets can be improved by the approaches such as data augmentation, ensembling, and transfer learning. In this study, we propose a new approach that utilizes transfer learning and ensemble methods to increase the accuracy rates of convolutional neural networks for classification tasks on small data sets. To this end, we generate different-sized sub-networks by fragmenting an existing large pre-trained network then gather those networks to form an ensemble.For ensemble scoring, we also suggest two new methods. Conducted experiments with the proposed technique, on a randomly sampled Cifar10 small subset dataset, reveals promising results.

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Computer‐aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks

Cited by 196 publications

References 37 publications

Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model

Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model

Multi‐Features‐Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

A hybrid approach based on transfer and ensemble learning for improving performances of deep learning models on small datasets

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