Breast ultrasound tumour classification: A Machine Learning—Radiomics based approach

2021 ◽  
Author(s):  
Arnab K. Mishra ◽  
Pinki Roy ◽  
Sivaji Bandyopadhyay ◽  
Sujit K. Das
Keyword(s):  
Machine Learning ◽  
Breast Ultrasound ◽  
Tumour Classification

scholarly journals Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Magda Marcon ◽  
Alexander Ciritsis ◽  
Cristina Rossi ◽  
Anton S. Becker ◽  
Nicole Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Texture Analysis ◽  
Area Under The Curve ◽  
Texture Features ◽  
Breast Ultrasound ◽  
Support Vector ◽  
Maximum Area ◽  
Svm Algorithm ◽  
Automated Breast Ultrasound

Abstract Background Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification. Methods This ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions. Results Skewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii). Conclusions TA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF.

Medical breast ultrasound image segmentation by machine learning

Ultrasonics ◽  
2019 ◽  
Vol 91 ◽  
pp. 1-9 ◽  
Author(s):  
Yuan Xu ◽  
Yuxin Wang ◽  
Jie Yuan ◽  
Qian Cheng ◽  
Xueding Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Ultrasound Image Segmentation

scholarly journals Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 367
Author(s):  
Ye-Jiao Mao ◽  
Hyo-Jung Lim ◽  
Ming Ni ◽  
Wai-Hin Yan ◽  
Duo Wai-Chi Wong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Shear Wave ◽  
Shear Wave Elastography ◽  
Breast Tumour ◽  
Learning Model ◽  
Ultrasound Elastography ◽  
Tumour Classification ◽  
Strain Elastography

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ³ 80%, while only half of them attained acceptable specificity ³ 95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

scholarly journals Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ting Xiao ◽  
Lei Liu ◽  
Kai Li ◽  
Wenjian Qin ◽  
Shaode Yu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Malignant Tumors ◽  
Breast Ultrasound ◽  
Breast Masses ◽  
Machine Learning Model ◽  
Independent Test ◽  
Combined Features ◽  
Malignant Lesions

This research aims to address the problem of discriminating benign cysts from malignant masses in breast ultrasound (BUS) images based on Convolutional Neural Networks (CNNs). The biopsy-proven benchmarking dataset was built from 1422 patient cases containing a total of 2058 breast ultrasound masses, comprising 1370 benign and 688 malignant lesions. Three transferred models, InceptionV3, ResNet50, and Xception, a CNN model with three convolutional layers (CNN3), and traditional machine learning-based model with hand-crafted features were developed for differentiating benign and malignant tumors from BUS data. Cross-validation results have demonstrated that the transfer learning method outperformed the traditional machine learning model and the CNN3 model, where the transferred InceptionV3 achieved the best performance with an accuracy of 85.13% and an AUC of 0.91. Moreover, classification models based on deep features extracted from the transferred models were also built, where the model with combined features extracted from all three transferred models achieved the best performance with an accuracy of 89.44% and an AUC of 0.93 on an independent test set.

Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods

2005 ◽  
Vol 34 (2) ◽  
pp. 129-139 ◽  
Author(s):  
Tim W. Nattkemper ◽  
Bert Arnrich ◽  
Oliver Lichte ◽  
Wiebke Timm ◽  
Andreas Degenhard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Breast Tumour ◽  
Learning Methods ◽  
Clinical Screening ◽  
Tumour Classification ◽  
Radiological Features ◽  
Machine Learning Methods

Machine learning for noise removal on breast ultrasound images

2010 ◽  
Author(s):  
Sumedha Sinha ◽  
Fong Ming Hooi ◽  
Zeeshan Syed ◽  
Renee Pinsky ◽  
Kai Thomenius ◽  
...  
Keyword(s):  
Machine Learning ◽  
Noise Removal ◽  
Breast Ultrasound ◽  
Ultrasound Images

Going beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis

2015 ◽  
Vol 41 (12) ◽  
pp. 3148-3162 ◽  
Author(s):  
Santosh S. Venkatesh ◽  
Benjamin J. Levenback ◽  
Laith R. Sultan ◽  
Ghizlane Bouzghar ◽  
Chandra M. Sehgal
Keyword(s):  
Machine Learning ◽  
Breast Ultrasound ◽  
Ultrasound Diagnosis ◽  
And Performance
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