Breast tumor classification using different features of quantitative ultrasound parametric images

Hsu, Soa-Min; Kuo, Wen‐Hung; Kuo, Fang-Chuan; Liao, Yin-Yin

doi:10.1007/s11548-018-01908-8

Cited by 33 publications

(14 citation statements)

References 41 publications

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“…Hsu et al [48] proposed the CAD for breast tumor classification using quantitative features extracted from ultrasound parametric images. The proposed CAD used a total number of 160 ultrasound images of which 80 were benign and 80 malignant lesions.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Framework for Breast Cancer Classification Using Machine Learning

Michael

et al. 2022

BioMed Research International

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Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that a larger number of ultrasound images are generated every day, and the number of radiologists able to analyze this medical data is very limited. This often results in misclassification of breast lesions, resulting in a high false-positive rate. In this article, we propose a computer-aided diagnosis (CAD) system that can automatically generate an optimized algorithm. To train machine learning, we employ 13 features out of 185 available. Five machine learning classifiers were used to classify malignant versus benign tumors. The experimental results revealed Bayesian optimization with a tree-structured Parzen estimator based on a machine learning classifier for 10-fold cross-validation. The LightGBM classifier performs better than the other four classifiers, achieving 99.86% accuracy, 100.0% precision, 99.60% recall, and 99.80% for the FI score.

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Section: Related Workmentioning

confidence: 99%

An Optimized Framework for Breast Cancer Classification Using Machine Learning

Michael

et al. 2022

BioMed Research International

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“…Data fusion based on different ultrasound parameters can play a complementary role and this method has been applied to the classification of tumors. [112][113][114][115] Rohrbach et al 112 developed prostate cancer risk assessment system based on multiple QUS parameters, including the scatterer property parameters (ESD and EAC) and the shape and scaling parameters (m and Ω) extracted from Nakagami distribution. Hsu et al 113 extracted several morphological features, B-mode texture features and Nakagami m-parameter from clinical cases to distinguish between benign and malignant breast tumors.…”

Section: Multiple Qus Parameters Fusion Monitoringmentioning

confidence: 99%

A Review of Quantitative Ultrasound-Based Approaches to Thermometry and Ablation Zone Identification Over the Past Decade

Zhou

et al. 2022

Ultrason Imaging

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Percutaneous thermal therapy is an important clinical treatment method for some solid tumors. It is critical to use effective image visualization techniques to monitor the therapy process in real time because precise control of the therapeutic zone directly affects the prognosis of tumor treatment. Ultrasound is used in thermal therapy monitoring because of its real-time, non-invasive, non-ionizing radiation, and low-cost characteristics. This paper presents a review of nine quantitative ultrasound-based methods for thermal therapy monitoring and their advances over the last decade since 2011. These methods were analyzed and compared with respect to two applications: ultrasonic thermometry and ablation zone identification. The advantages and limitations of these methods were compared and discussed, and future developments were suggested.

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“…According to the research by Hsu SM et al morphologicalfeature parameters (e.g., standard deviation of the shortest distance), texture features (e.g., variance), and the Nakagami parameter are combined to extract the physical features of breast ultrasound images, they classified the data using FCM clustering and achieved an accuracy of 89.4%, a specificity of 86.3%, and a sensitivity of 92.5%. Compared with logistic regression and SVM classifiers, the maximum discrimination performance of the optimal feature collection was independent of the type of classifier, indicating that the combination of different feature parameters should be functionally complementary to improve the performance of breast cancer classification (53). Zhang et al constructed a two-layer DL architecture to extract the shear-wave elastography (SWE) features by combining feature learning and feature selection.…”

Section: Feature Extractionmentioning

confidence: 99%

Artificial Intelligence in Medical Imaging of the Breast

Lei

Yin

et al. 2021

Front. Oncol.

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Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.

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Breast tumor classification using different features of quantitative ultrasound parametric images

Cited by 33 publications

References 41 publications

An Optimized Framework for Breast Cancer Classification Using Machine Learning

An Optimized Framework for Breast Cancer Classification Using Machine Learning

A Review of Quantitative Ultrasound-Based Approaches to Thermometry and Ablation Zone Identification Over the Past Decade

Artificial Intelligence in Medical Imaging of the Breast

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