SVM-Based Microcalcification Detection in Digital Mammograms

2008 ◽  
Author(s):  
Jin Ye ◽  
Sheng Zheng ◽  
Changcai Yang
Keyword(s):  
Microcalcification Detection

Visually lossless threshold determination for microcalcification detection in wavelet compressed mammograms

2003 ◽  
Vol 13 (10) ◽  
pp. 2390-2396 ◽  
Author(s):  
O. Kocsis ◽  
L. Costaridou ◽  
L. Varaki ◽  
E. Likaki ◽  
C. Kalogeropoulou ◽  
...  
Keyword(s):  
Threshold Determination ◽  
Microcalcification Detection

The use of joint two-view information for improving computerized microcalcification detection accuracy

2003 ◽  
pp. 358-362 ◽  
Author(s):  
Berkman Sahiner ◽  
Lubomir M. Hadjiiski ◽  
Chan Heang-Ping ◽  
Nicholas Petrick ◽  
Mark A. Helvie
Keyword(s):  
Detection Accuracy ◽  
Microcalcification Detection

scholarly journals Study of Hardware Implementation on Size of the Microcalcification Detection Using Embedded Systems

2018 ◽  
Vol 7 (3.27) ◽  
pp. 415
Author(s):  
G R. Jothilakshmi ◽  
P Mohana Priya ◽  
V K. Suvithra
Keyword(s):  
Reflection Coefficient ◽  
Cancer Detection ◽  
Hardware Implementation ◽  
Early Stage ◽  
Mass Density ◽  
Linear Mapping ◽  
The People ◽  
Malignant Breast ◽  
Mammography Image ◽  
Microcalcification Detection

Detection of microcalcification in glandular breasts is highly critical for early stage cancer detection since, it is very small in size. To detect such smaller microcalcification a hardware device is needed, which is created by the using the digital mammography image from DDSM database the image of malignant breast is acquainted. Two levels of binning is carried out with respect to the RoI to calculate the range of reflection coefficient. Linear mapping of reflection coefficient with mass density is projected as 3D and simultaneously the size of respective second bin is  calculated to derive the size if the microcalcification .This process is then implemented on hardware to make it more commercial for the people to detect the cancer at an early stage. 

scholarly journals Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Gabriele Valvano ◽  
Gianmarco Santini ◽  
Nicola Martini ◽  
Andrea Ripoli ◽  
Chiara Iacconi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
False Positive Rate ◽  
Novel Approach ◽  
Positive Rate ◽  
Microcalcification Detection ◽  
Microcalcification Clusters

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.

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