Breast density classification to reduce false positives in CADe systems

Vállez, Noelia; Bueno, Gloria; Déniz, Óscar; Dorado, Julián; Seoane, José A.; Pastor, Carlos

doi:10.1016/j.cmpb.2013.10.004

Cited by 32 publications

(23 citation statements)

References 50 publications

(58 reference statements)

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“…To automatically detect masses, the feature-fusion based approach was developed in [23] using canonical correlation analysis (CCA). In [24], a hierarchical classification procedure was developed based on the combined linear discriminant analysis (LDA). However in [25] a set of 349 ROI masses were used to characterize of breast mass by using three classifiers Bayesian, Fisher and SVM.…”

Section: Pre-and Post-processing Methodsmentioning

confidence: 99%

DeepCAD: A Computer-Aided Diagnosis System for Mammographic Masses Using Deep Invariant Features

Abbas

2016

Computers

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Abstract:The development of a computer-aided diagnosis (CAD) system for differentiation between benign and malignant mammographic masses is a challenging task due to the use of extensive pre-and post-processing steps and ineffective features set. In this paper, a novel CAD system is proposed called DeepCAD, which uses four phases to overcome these problems. The speed-up robust features (SURF) and local binary pattern variance (LBPV) descriptors are extracted from each mass. These descriptors are then transformed into invariant features. Afterwards, the deep invariant features (DIFs) are constructed in supervised and unsupervised fashion through multilayer deep-learning architecture. A fine-tuning step is integrated to determine the features, and the final decision is performed via softmax linear classifier. To evaluate this DeepCAD system, a dataset of 600 region-of-interest (ROI) masses including 300 benign and 300 malignant masses was obtained from two publicly available data sources. The performance of DeepCAD system is compared with the state-of-the-art methods in terms of area under the receiver operating characteristics (AUC) curve. The difference between AUC of DeepCAD and other methods is statistically significant, as it demonstrates a sensitivity (SN) of 92%, specificity (SP) of 84.2%, accuracy (ACC) of 91.5% and AUC of 0.91. The experimental results indicate that the proposed DeepCAD system is reliable for providing aid to radiologists without the need for explicit design.

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Section: Pre-and Post-processing Methodsmentioning

confidence: 99%

DeepCAD: A Computer-Aided Diagnosis System for Mammographic Masses Using Deep Invariant Features

Abbas

2016

Computers

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“…Feature extraction, which is the third stage, is an undoubtedly important task for pattern recognition and is implemented with a remarkable number of techniques in several studies. Specifically, there are statistical techniques [20–25], model-based techniques [26, 27], graph-theoretic approaches [28], and signal processing techniques that compute breast tissue features from pixel characteristics [22, 29] or frequency spectrum [21, 23, 24, 30–32] for breast cancer diagnosis on a mammographic image. Additionally, there are various studies using mammographic features [20, 33–36] like shape, spicule index, contour, size, density, and brightness.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

Esener

Ergin

Yüksel

2017

Journal of Healthcare Engineering

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A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD) system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA) project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI) images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.

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“…Since some calcifications are very small and the density difference between the healthy tissue and masses may be very low, diagnosis process will be difficult. Therefore, considering the importance of the accurate diagnosis, Computer-Aided Diagnosis (CAD) techniques were presented in recent years [1][2][3][4][5][6][7][8][9][10][11] to help physicians and also reduce False Positive Rate (FPR) to perform diagnosis action faster, more easily and more accurately.…”

Section: Introductionmentioning

confidence: 99%

Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution

Pak

Kanan

Alikhassi

2015

Computer Methods and Programs in Biomedicine

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Breast density classification to reduce false positives in CADe systems

Cited by 32 publications

References 50 publications

DeepCAD: A Computer-Aided Diagnosis System for Mammographic Masses Using Deep Invariant Features

DeepCAD: A Computer-Aided Diagnosis System for Mammographic Masses Using Deep Invariant Features

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution

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