Classification of breast mass in ultrasound images using CAD: A survey

Menon, Radhika V.; Raha, Poulami; Chakrabarti, Indrajit

doi:10.1109/icsmb.2016.7915081

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Extensive research on mass detection has been done, and multiple studies have shown high performance. [131][132][133][134][135] The detection of mass can generally divide into two steps: localization of mass and classification. With the help of deep CNNs, these two steps can be realized easier but more efficiently.…”

Section: Mass Detectionmentioning

confidence: 99%

A systematic survey of deep learning in breast cancer

Zhou

Wang

et al. 2021

Int J Intell Syst

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In recent years, we witnessed a speeding development of deep learning in computer vision fields like categorization, detection, and semantic segmentation. Within several years after the emergence of AlexNet, the performance of deep neural networks has already surpassed human being experts in certain areas and showed great potential in applications such as medical image analysis. The development of automated breast cancer detection systems that integrate deep learning has received wide attention from the community. Breast cancer, a major killer of females that results in millions of deaths, can be controlled even be cured given that it is detected at an early stage with sophisticated systems. In this paper, we reviewed breast cancer diagnosis, detection, and segmentation computer-aided (CAD) systems based on state-of-the-art deep convolutional neural networks. The available data sets also indirectly determine CAD systems' performance, so we introduced and discussed the details of public data sets. The challenges remaining in CAD systems for breast cancer are discussed at the end of this paper. The highlights of this survey mainly come from three following aspects. First, we covered a wide range of the basics of breast cancer from imaging modalities to popular databases in the community; Second, we presented the key elements in deep learning to form the compactness for methods mentioned in reviewed papers; Third and lastly, the summative details in each reviewed paper are provided so that interested readers can have a refined version of these works without referring to original papers.Therefore, this systematic survey suits readers with varied backgrounds and will be beneficial to them.

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Section: Mass Detectionmentioning

confidence: 99%

A systematic survey of deep learning in breast cancer

Zhou

Wang

et al. 2021

Int J Intell Syst

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“…In the latter case the location and possibly the shape of the lesion have been obtained by some method and the task is to classify the lesion as benign or malignant. An extensive review of available methods for detection and classification of breast lesions in US images is presented by Cheng et al (2010) and more recently by Menon et al (2016). In the following mainly the most recent developments will be reviewed.…”

Section: Introductionmentioning

confidence: 99%

Study of classification of breast lesions using texture GLCM features obtained from the raw ultrasound signal

Nieniewski¹,

Chmielewski²

2020

Image Anal Stereol

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Most of the methods of classification of breast lesions in ultrasound (US) images have been tested on B-mode images from the commercial equipment. The new possibility of further analysis of this problem showed up with the availability of a public database containing original raw radio frequency (RF) signals. In particular, it appeared that the original texture might contain diagnostic information which could be modified in the typical image processing and which is more difficult to perceive than the details of lesion shape/contour. In this paper a detailed analysis of the lesion texture is conducted by means of the decision trees and logistic regression. The decision trees turned out useful mainly for selecting texture features to be used in the stepwise logistic regression. The RF signals database of 200 breast lesions was used for testing the performance of the benign vs malignant lesion classifier. The Gray Level Cooccurrence Matrix (GLCM) was calculated with the vertical/horizontal offset of up to five pixels. For each of these matrices six features were calculated resulting in a total of 210 features. Using these features a sufficient number of decision trees were generated to calculate pseudo-Receiver Operating Characteristics (ROCs). The outcome of this process is a collection of generated trees for which the employed features are known. These features were then used for generating generalized linear model by means of stepwise logistic regression. The analyzed regression models included the coefficients of up-to-the second degree terms. The texture features were further completed by a single shape feature, that is tumor circularity (TC). The automatic procedure for finding the exact mask of a lesion is also provided for the conditions when the acoustic shadowing makes it impossible to obtain the entire contour reliably and a half-contour has to be used. The selected logistic regression models gave ROCs with the Area Under Curve (AUC) of up to 0.83 and the 95 \% confidence region (0.63 0.96). Analyzing classification results one comes to the conclusion that the tumor circularity, which is the most informative among shape/contour features, is not essential against the background of textural features. The reported study shows that a relatively straightforward procedure can be employed to obtain benign vs malignant classifier comparable with that originally used for the database of the raw RF signals and based on the more complicated segmentation of the parameter maps of homodyned K distribution.

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Classification of breast mass in ultrasound images using CAD: A survey

Cited by 2 publications

References 8 publications

A systematic survey of deep learning in breast cancer

A systematic survey of deep learning in breast cancer

Study of classification of breast lesions using texture GLCM features obtained from the raw ultrasound signal

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