Classification of breast ultrasound images based on posterior feature

Tianur,; Nugroho, Hanung Adi; Sahar, Muzni; Ardiyanto, Igi; Indrastuti, Reni; Choridah, Lina

doi:10.1109/icitsi.2016.7858239

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Computer-aided diagnosis of breast cancer based on traditional image analysis has been studied for decades (12); for example, fractal dimension estimation (13), computation of the area of breast lesions based on region growth (14), and classification of breast tumors (15). However, these traditional methods lack robustness because they rely on hand-crafted features.…”

Section: Original Articlementioning

confidence: 99%

Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy

Gao¹,

Liu²,

Zhu³

et al. 2021

Quant Imaging Med Surg

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Background: The successful recognition of benign and malignant breast nodules using ultrasound images is based mainly on supervised learning that requires a large number of labeled images. However, because high-quality labeling is expensive and time-consuming, we hypothesized that semi-supervised learning could provide a low-cost and powerful alternative approach. This study aimed to develop an accurate semisupervised recognition method and compared its performance with supervised methods and sonographers.Methods: The faster region-based convolutional neural network was used for nodule detection from ultrasound images. A semi-supervised classifier based on the mean teacher model was proposed to recognize benign and malignant nodule images. The general performance of the proposed method on two datasets (8,966 nodules) was reported.Results: The detection accuracy was 0.88±0.03 and 0.86±0.02, respectively, on two testing sets (1,350 and 2,220 nodules). When 800 labeled training nodules were available, the proposed semi-supervised model plus 4,396 unlabeled nodules performed better than the supervised learning model (area under the curve (AUC): 0.934±0.026 vs. 0.83±0.050; 0.916±0.022 vs. 0.815±0.049). The performance of the semi-supervised model trained on 800 labeled and 4,396 unlabeled nodules was close to that of the supervised learning model trained on a massive number of labeled nodules (n=5,196) (AUC: 0.934±0.026 vs. 0.952±0.027; 0.916±0.022 vs. 0.918±0.017). Moreover, the semi-supervised model was better than the average accuracy of five human sonographers (AUC: 0.922 vs. 0.889). Conclusions:The semi-supervised model can achieve excellent performance for nodule recognition and be useful for medical sciences. The method reduced the number of labeled images required for training, thus significantly alleviating the difficulty in data preparation of medical artificial intelligence.

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Section: Original Articlementioning

confidence: 99%

Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy

Gao¹,

Liu²,

Zhu³

et al. 2021

Quant Imaging Med Surg

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“…Kou et al 5 calculated the three indices of the tumor and its contour and then used multilayer perception to classify breast cancer. Tianur et al 6 first eliminated speckle noise in ultrasound breast images, then determined the region of breast lesions using a region growing method and artificial regions of interest (ROIs), and finally extracted features and conducted classification using a multilayer perceptron. Mohammed et al 7 used multifractal dimensions to automate the characterization of breast cancer and used a back-propagation algorithm to train a neural network to classify breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Breast ultrasound lesion classification based on image decomposition and transfer learning

et al. 2020

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Purpose In medical image analysis, deep learning has great application potential. Discovering a method for extracting valuable information from medical images and integrating that information closely with medical treatment has recently become a major topic of interest. Because obtaining large volumes of breast lesion ultrasound image data is difficult, transfer learning is usually employed to obtain benign and malignant classification of breast lesions. However, because of blurred unclear regions of interest in breast lesion ultrasound images and severe speckle noise interference, convolutional neural networks have proven ineffective in extracting features, thus providing unreliable classification results. Methods This study employs image decomposition to obtain fuzzy enhanced and bilateral filtered images to enrich input information of breast lesions. Fuzzy enhanced, bilateral filtered, and original ultrasound images comprise multifeature data, which are presented as inputs to a pre‐trained model to realize knowledge fusion. Therefore, effective features of breast lesions are extracted and then used to train fully connected layers with ground truths provided by a doctor to accomplish the classification. Results A pre‐trained VGG16 model was used to extract features from multifeature data, and these features were fused to train the fully connected layers to realize classification. The performance score reported is as follows: accuracy of 93%, sensitivity of 95%, specificity of 88%, F1 score of 0.93, and AUC of 0.97. Conclusions Compared with using a single original ultrasound image for feature extraction, multifeature data based on image decomposition enables the pre‐trained model to extract more relevant features, thereby providing better classification results than those from traditional transfer learning techniques.

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Fully-automated deep learning pipeline for segmentation and classification of breast ultrasound images

Podda

Balia

Barra

et al. 2022

Journal of Computational Science

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Classification of breast ultrasound images based on posterior feature

Cited by 4 publications

References 9 publications

Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy

Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy

Breast ultrasound lesion classification based on image decomposition and transfer learning

Fully-automated deep learning pipeline for segmentation and classification of breast ultrasound images

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