Journal ArticleDOI
Automatic computer-aided diagnosis system for mass detection and classification in mammography
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TLDR
A complete CAD system for mass detection and diagnosis, which consists of four steps where the preprocessing where the image is enhanced and the noise removed, and the support vector machine (SVM) is used to classify the abnormalities as malignant or benign.Abstract:
Mammography is currently the most powerful technique for early detection of breast cancer. To assist radiologists to better interpret mammogram images, computer-aided detection and diagnosis (CAD) systems have been proposed. This paper proposes a complete CAD system for mass detection and diagnosis, which consists of four steps. The first step consists of the preprocessing where the image is enhanced and the noise removed. In the second step, the abnormalities are segmented using the proposed HRAK algorithm. In the third step, the false positives are reduced using texture and shape features and the bagged trees classifier. Finally, the support vector machine (SVM) is used to classify the abnormalities as malignant or benign. The proposed CAD system is verified with both the MIAS and CBIS-DDSM databases. The experimental results proved to be successful. The accuracy detection rate achieves 93,15% for sensitivity and 0,467 FPPI for MIAS and 90,85% for sensitivity and 0,65 FPPI for CBIS-DDSM. The accuracy classification rate achieves 94,2% and the AUC 0,95 for MIAS and 90,44% and 0,9 for CBIS-DDSM.read more
Citations
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Journal ArticleDOI
Deconv-transformer (DecT): A histopathological image classification model for breast cancer based on color deconvolution and transformer architecture
TL;DR: Deconv-Transformer (DecT) as discussed by the authors uses a self-attention mechanism to match the independent properties of the HED channel information obtained by the color deconvolution, which can compensate for the information loss in the process of transferring RGB images to HED images.
Journal ArticleDOI
Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm
TL;DR: In this article , the Modified Entropy Whale Optimization Algorithm (MEWOA) is proposed based on fusion for deep feature extraction and perform the classification, which achieved the maximum accuracy achieved in INbreast dataset is 99.7%, MIAS dataset has 99.8% and CBIS-DDSM has 93.8%.
Journal ArticleDOI
Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor
TL;DR: In this article , a comparative analysis of the proficiencies of various textures and geometric features in the diagnosis of breast masses on mammograms is presented, where four state-of-the-art filter-based feature selection algorithms (Relief-F, Pearson correlation coefficient, neighborhood component analysis, and term variance) are employed to select the top 20 most discriminative features.
Journal ArticleDOI
Information bottleneck-based interpretable multitask network for breast cancer classification and segmentation
Junxia Wang,Yuanjie Zheng,Junlin Ma,Xinmeng Li,Chongjing Wang,James Gee,Haipeng Wang,Wenhui Huang +7 more
TL;DR: Li et al. as mentioned in this paper proposed an interpretable multitask information bottleneck network (MIB-Net) to accomplish simultaneous breast tumor classification and segmentation, which maximizes the mutual information between the latent representations and class labels while minimizing information shared by the latent representation and inputs.
Journal ArticleDOI
Breast Mass Detection in Mammography Based on Image Template Matching and CNN.
TL;DR: Wang et al. as discussed by the authors proposed a novel breast mass detection method to detect breast mass on a mammographic image by stimulating the process of human detection, which is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found.
References
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Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
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Ensemble Methods: Foundations and Algorithms
TL;DR: An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks and gives the necessary groundwork to carry out further research in this evolving field.
The Mammographic Image Analysis Society digital mammogram database
John Suckling,J. Parker,S. Astley,I. Hutt,C. Boggis,Ian W. Ricketts,E. Stamatakis,N. Cerneaz,SL Kok,P. Taylor,D. Betal,J. Savage +11 more
Proceedings ArticleDOI
EGNet: Edge Guidance Network for Salient Object Detection
TL;DR: In this article, an edge guidance network (EGNet) is proposed for salient object detection with three steps to simultaneously model these two kinds of complementary information in a single network, which can help locate salient objects especially their boundaries more accurately.
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