Improved breast cancer classification through combining graph convolutional network and convolutional neural network
Aim In a pilot study to improve detection of malignant lesions in breast mammograms, we
aimed to develop a new method called BDR-CNN-GCN, combining two advanced neural …
aimed to develop a new method called BDR-CNN-GCN, combining two advanced neural …
[HTML][HTML] A curated mammography data set for use in computer-aided detection and diagnosis research
Published research results are difficult to replicate due to the lack of a standard evaluation
data set in the area of decision support systems in mammography; most computer-aided …
data set in the area of decision support systems in mammography; most computer-aided …
Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling
YD Zhang, C Pan, X Chen, F Wang - Journal of computational science, 2018 - Elsevier
Aim Abnormal breast appears similar as dense breast in mammography, which makes it a
challenge for radiologists to identify. Scholars have proposed numerous computer-vision …
challenge for radiologists to identify. Scholars have proposed numerous computer-vision …
Utilization of DenseNet201 for diagnosis of breast abnormality
As one of the leading killers of females, breast cancer has become one of the heated
research topics in the community of clinical medical science and computer science. In the …
research topics in the community of clinical medical science and computer science. In the …
ResNet-SCDA-50 for breast abnormality classification
(Aim) Breast cancer is the most common cancer in women and the second most common
cancer worldwide. With the rapid advancement of deep learning, the early stages of breast …
cancer worldwide. With the rapid advancement of deep learning, the early stages of breast …
Abnormal breast detection in mammogram images by feed-forward neural network trained by Jaya algorithm
(Aim) Abnormal breast can be diagnosed using the digital mammography. Traditional
manual interpretation method cannot yield high accuracy.(Method) In this study, we …
manual interpretation method cannot yield high accuracy.(Method) In this study, we …
Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded …
Y Zhang, X Wu, S Lu, H Wang, P Phillips… - Simulation, 2016 - journals.sagepub.com
In this study, we proposed a smart detection method for abnormal breasts in digital
mammography. Firstly, preprocessing was carried out to deaden noises, enhance images …
mammography. Firstly, preprocessing was carried out to deaden noises, enhance images …
Computer aided diagnosis-medical image analysis techniques
B Halalli, A Makandar - Breast imaging, 2018 - books.google.com
Breast cancer is the second leading cause of death among women worldwide.
Mammography is the basic tool available for screening to find the abnormality at the earliest …
Mammography is the basic tool available for screening to find the abnormality at the earliest …
Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform
Abnormal breast can be diagnosed using the digital mammography. Traditional manual
interpretation method cannot yield high accuracy. In this study, we proposed a novel …
interpretation method cannot yield high accuracy. In this study, we proposed a novel …
LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues
In computer-aided diagnosis one of the crucial steps to classify suspicious lesions is the
extraction of features. Texture analysis methods have been used in the analysis and …
extraction of features. Texture analysis methods have been used in the analysis and …