A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation

2015 ◽  
Vol 42 (7) ◽  
pp. 3859-3869 ◽  
Author(s):  
Jinghui Chu ◽  
Hang Min ◽  
Li Liu ◽  
Wei Lu
Keyword(s):  
Breast Mass ◽  
Mass Detection ◽  
Detection Scheme ◽  
Superpixel Segmentation ◽  
Computer Aided

scholarly journals Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework

2014 ◽  
Vol 13s1 ◽  
pp. CIN.S13885
Author(s):  
Maxine Tan ◽  
Jiantao Pu ◽  
Bin Zheng
Keyword(s):  
Selection Process ◽  
Learning Task ◽  
Classification Performance ◽  
Optimal Topology ◽  
Support Vector ◽  
Mass Detection ◽  
Detection Scheme ◽  
Linear Discriminant ◽  
Computer Aided ◽  
Fixed Topology

In the field of computer-aided mammographic mass detection, many different features and classifiers have been tested. Frequently, the relevant features and optimal topology for the artificial neural network (ANN)-based approaches at the classification stage are unknown, and thus determined by trial-and-error experiments. In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named “Phased Searching with NEAT in a Time-Scaled Framework” was analyzed using a dataset with 800 malignant and 800 normal tissue regions in a 10-fold cross-validation framework. The classification performance measured by the area under a receiver operating characteristic (ROC) curve was 0.856 ± 0.029. The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees. The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM. Furthermore, the Phased Searching method required fewer features and discarded superfluous structure or topology, thus incurring a lower feature computational and training and validation time requirement. Analyses performed on the network complexities evolved by Phased Searching indicate that it can evolve optimal network topologies based on its complexification and simplification parameter selection process. From the results, the study also concluded that the three classifiers – SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework – are performing comparably well in our mammographic mass detection scheme.

CSABlock-based Cascade RCNN for Breast Mass Detection in Mammogram

2020 ◽  
Author(s):  
He Xiao ◽  
Qingfeng Wang ◽  
Zhiqin Liu ◽  
Jun Huang ◽  
Yuwei Zhou ◽  
...  
Keyword(s):  
Breast Mass ◽  
Mass Detection

Towards improved breast mass detection using dual-view mammogram matching

2021 ◽  
pp. 102083
Author(s):  
Yutong Yan ◽  
Pierre-Henri Conze ◽  
Mathieu Lamard ◽  
Gwenolé Quellec ◽  
Béatrice Cochener ◽  
...  
Keyword(s):  
Breast Mass ◽  
Mass Detection

Breast mass detection by US: a phantom study.

Radiology ◽  
1985 ◽  
Vol 156 (3) ◽  
pp. 773-775 ◽  
Author(s):  
W J Davros ◽  
E L Madsen ◽  
J A Zagzebski
Keyword(s):  
Phantom Study ◽  
Breast Mass ◽  
Mass Detection

Combining two mammographic projections in a computer aided mass detection method

2007 ◽  
Vol 34 (3) ◽  
pp. 898-905 ◽  
Author(s):  
Saskia van Engeland ◽  
Nico Karssemeijer
Keyword(s):  
Detection Method ◽  
Mass Detection ◽  
Computer Aided

Breast mass detection and diagnosis using fused features with density

2019 ◽  
Vol 27 (2) ◽  
pp. 321-342 ◽  
Author(s):  
Zhiqiong Wang ◽  
Yukun Huang ◽  
Mo Li ◽  
Hao Zhang ◽  
Chen Li ◽  
...  
Keyword(s):  
Breast Mass ◽  
Mass Detection ◽  
Detection And Diagnosis

Is the Computer-Aided Detection Scheme for Lung Nodule Also Useful in Detecting Lung Cancer?

2008 ◽  
Vol 32 (4) ◽  
pp. 570-575 ◽  
Author(s):  
Jin Mo Goo ◽  
Hyae Young Kim ◽  
Jeong Won Lee ◽  
Hyun Ju Lee ◽  
Chang Hyun Lee ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lung Nodule ◽  
Computer Aided Detection ◽  
Detection Scheme ◽  
Computer Aided

False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification

2018 ◽  
Vol 160 ◽  
pp. 75-83 ◽  
Author(s):  
Sami Dhahbi ◽  
Walid Barhoumi ◽  
Jaroslaw Kurek ◽  
Bartosz Swiderski ◽  
Michal Kruk ◽  
...  
Keyword(s):  
Texture Analysis ◽  
False Positive ◽  
Mass Detection ◽  
False Positive Reduction ◽  
Computer Aided
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