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Computers 2016, 5(4), 28; doi:10.3390/computers5040028

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

College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Academic Editor: Kartik Gopalan
Received: 5 August 2016 / Revised: 28 September 2016 / Accepted: 26 October 2016 / Published: 31 October 2016
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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. View Full-Text
Keywords: breast cancer; mammography; computer-aided diagnosis system; deep learning; neural networks; image processing; local binary pattern; speed-up robust features breast cancer; mammography; computer-aided diagnosis system; deep learning; neural networks; image processing; local binary pattern; speed-up robust features
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Abbas, Q. DeepCAD: A Computer-Aided Diagnosis System for Mammographic Masses Using Deep Invariant Features. Computers 2016, 5, 28.

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