Automated detection and classification of mass from breast ultrasound images

Menon, Radhika V.; Raha, Poulami; Kothari, Shweta; Chakraborty, Sumit; Chakrabarti, Indrajit; Karim, Rezaul

doi:10.1109/ncvpripg.2015.7490070

Cited by 19 publications

(15 citation statements)

References 12 publications

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“…Recently, Lingyun Cai et al proposed a novel phase-based texture descriptor for a robust support vector machine (SVM) classifier to discriminate benign and malignant tumors in BUS images [ 7 ]. Similarly, Menon R V et al adopted SVM method for classification through textural, morphological, and histogram feature metrics with principal component analysis (PCA) for dimension reduction [ 8 ]. In [ 9 ], a novel feature selection approach based on dual evaluation criteria was proposed to select 457 texture and shape features, with which Artificial Neural Network (ANN) and SVM were both used for classifying benign and malignant breast tumors.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination

Xiao

Liu

et al. 2018

BioMed Research International

126

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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.

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Section: Introductionmentioning

confidence: 99%

Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination

Xiao

Liu

et al. 2018

BioMed Research International

126

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“…(27), TP stands for malignant classification in the case of malignant tumour, FN stands for benign classification in the case of malignant tumour, TN stands for benign classification in the case of benign tumour and FP stands for malignant classification in the case of benign tumour. In another CAD system that uses the same ultrasound image database with this study, benign/malignant classification was realized using an SVM classifier and a correctness of 95:7%, sensitivity of 87:5% and specificity of 100% were gained [30]. In another study where the BI-RADS category classification was realized using logistic regression, a correctness rate of 81% was acquired [3].…”

Section: Classificationmentioning

confidence: 98%

“…In lots of CAD systems, texture and morphological features [29] have been used together to increase the classification success rate [13,28,[30][31][32][33][34][35][36][37]. Morphological features are independent of various ultrasound systems or machines in the diagnosis of breast cancer masses.…”

Section: Morphological (Shape Margin) Features Extractionmentioning

confidence: 99%

“…There are many feature selection systems in the literature. They are principal component analysis (PCA) [30,44], artificial intelligence algorithms [31], bidirectional search [29], t-test for selecting class-descriptor features and statistical significance tests such as Wilcoxon-Mann-Whitney (WMW) test [52] and backward feature elimination method [53].…”

Section: Feature Selectionmentioning

confidence: 99%

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A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system

Uzunhisarcikli

Göreke

2018

Sādhanā

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Ultrasound imaging is an imaging technique for early detection of breast cancer. Breast Imaging Reporting and Data System (BI-RADS) lexicon, developed by The American College of Radiology, provides a standard for expert doctors to interpret the ultrasound images of breast cancer. This standard describes the features to classify the tumour as benign or malignant and it also categorizes the biopsy requirement as a percentage. Biopsy is an invasive method that doctors use for diagnosis of breast cancer. Computer-aided detection (CAD)/diagnosis systems that are designed to include the feature standards used in benign/malignant classification help the doctors in diagnosis but they do not provide enough information about the BI-RADS category of the mass. These systems classify the benign tumours with 90% biopsy possibility (BI-RADS-4) and with 2% biopsy possibility (BI-RADS-2) in the same category. There are some studies in the literature that make category classification via commonly used classifier methods but their success rates are low. In this study, a twolayer, high-success-rate classifier model based on Type-2 fuzzy inference is developed, which classifies the tumour as benign or malignant with its BI-RADS category by incorporating the opinions of the expert doctors. A 99.34% success rate in benign/malignant classification and a 92% success rate in category classification (BI-RADS 2, 3, 4, 5) were obtained in the accuracy tests. These results indicate that the CAD system is valuable as a means of providing a second diagnostic opinion when radiologists carry out mass diagnosis.

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“…Secondly, many methods are to model the breast ultrasound images using multiple features (texture features and morphological features) with single classifier [ 9 – 12 ]. For example, Menon et al [ 10 ] extracted the textural, morphological, and histogram features of tumor ultrasound images and used SVM to classify tumors. Gonzelezluna et al [ 12 ] extracted 41 morphological features and 96 texture features to analyze the classification effects of 7 classifiers.…”

Section: Related Workmentioning

confidence: 99%

A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images

Wei

et al. 2020

Computational and Mathematical Methods in Medicine

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The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to women’s health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.

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Automated detection and classification of mass from breast ultrasound images

Cited by 19 publications

References 12 publications

Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination

Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Masses Discrimination

A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system

A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images

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