Classification of malignant tumors in breast ultrasound using a pretrained deep residual network model and support vector machine

Shia, Wei‐Chung; Chen, Dar‐Ren

doi:10.1016/j.compmedimag.2020.101829

Cited by 56 publications

(34 citation statements)

References 26 publications

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“…Previous related studies used image segmentation [ 3 , 4 ] or lesion texture [ 5 , 6 ] to generate a pattern or model for malignant classification. In addition, several studies incorporated established significant features of the whole image into a deep learning network for malignant or benign tumor classification [ 7 , 8 , 9 , 10 , 11 ]. While all these previous studies had a classification accuracy of over 85% and showed good preliminary performance, providing only the benign and/or malignant classification of an image is insufficient for clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound

et al. 2021

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In this study, we applied semantic segmentation using a fully convolutional deep learning network to identify characteristics of the Breast Imaging Reporting and Data System (BI-RADS) lexicon from breast ultrasound images to facilitate clinical malignancy tumor classification. Among 378 images (204 benign and 174 malignant images) from 189 patients (102 benign breast tumor patients and 87 malignant patients), we identified seven malignant characteristics related to the BI-RADS lexicon in breast ultrasound. The mean accuracy and mean IU of the semantic segmentation were 32.82% and 28.88, respectively. The weighted intersection over union was 85.35%, and the area under the curve was 89.47%, showing better performance than similar semantic segmentation networks, SegNet and U-Net, in the same dataset. Our results suggest that the utilization of a deep learning network in combination with the BI-RADS lexicon can be an important supplemental tool when using ultrasound to diagnose breast malignancy.

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Section: Introductionmentioning

confidence: 99%

Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound

et al. 2021

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“…The proposed DRN-SVM was applied to 2099 unlabeled 2D breast ultrasound images. Results in [27] proven the potential applicability of the proposed approach to detect and classify benign and malignant breast tumors.…”

Section: Related Workmentioning

confidence: 71%

“…As illustrated in [27], a new method using pre-trained Deep Residual Network (DRN) model and Support Vector Machine (SVM) was proposed. DRN-SVM was used to detect and classify benign and malignant breast tumors using ultrasound images.…”

Section: Related Workmentioning

confidence: 99%

Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

Shaban¹

2021

Preprint

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Breast cancer is one of the most common types of cancer that affects women globally and it is the primary cause of death. Early detection of breast cancer is a vital process that can facilitate appropriate treatment, stop the progression of cancer cells, and reduce morbidity and mortality. Artificial Intelligence (AI) and Machine Learning (ML) are the most popular methods that can be used to detect and classify breast cancer accurately. In this paper, a new strategy for classifying breast cancer using Neutrosophic Techniques (NTs) and machine learning techniques is introduced, which is called Breast Cancer Classification Strategy (BC2S). The proposed BC2S consists of two phases, which are; Data Preprocessing Phase (DP2) and Breast Cancer Classification Phase (BC2P). The main aim of the data preprocessing phase is to; (i) extract features from mammogram images and then remove the outlier items, (ii) select the most effective and informative features from those extracted features using new feature selection method called Efficient Ant Colony Optimization (EACO), and (iii) convert the selected features from classical domain into neutrosophic domain using NTs to give accurate classification through the next classification phase BC2P. The proposed classification model uses Deep Neural Network (DNN) to determine whether the patient is normal or infected with benign or malignant cancer. According to experimental results, the proposed strategy outperforms other competitors in terms of accuracy, precision, recall, and F-measure.

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“…When a neural network is provided with a large number of example images that have been classified by humans, it is able to determine common patterns in those examples and to use this 'knowledge' to independently classify future unclassified images. 2 Examples of practical areas where computer vision is applied are medical diagnosis (e.g., detecting malignant tumors, see Shia and Chen 2021), surveillance (e.g., by face recognition, see Harikrishnan, Sudarsan, Sadashiv et al 2019), and self-driving cars (e.g., detecting the presence of pedestrians in traffic situations, see Hasan, Liao, Li et al 2020). Our example application in the present paper uses a customized computer vision algorithm for pedestrian detection.…”

Section: Computer Visionmentioning

confidence: 99%

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Bernasco¹,

Hoeben²,

Koelma³

et al. 2021

Preprint

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Social scientists increasingly use video data, but large-scale analysis of its content is often constrained by scarce manual coding resources. Upscaling may be possible with the application of automated coding procedures, which are being developed in the field of computer vision. Here, we introduce computer vision to social scientists, review the state-of-the-art in relevant subfields, and provide a working example of how computer vision can be applied in empirical sociological work. Our application involves defining a ground truth by human coders, developing an algorithm for automated coding, testing the performance of the algorithm against the ground truth, and run the algorithm on a large-scale dataset of CCTV images. The working example concerns monitoring social distancing behavior in public space over more than a year of the COVID-19 pandemic. Finally, we discuss prospects for the use of computer vision in empirical social science research and address technical and ethical limitations.

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Classification of malignant tumors in breast ultrasound using a pretrained deep residual network model and support vector machine

Cited by 56 publications

References 26 publications

Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound

Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound

Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

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