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.
Highlights
Breast cancer is one of the prominent reasons for deaths of women, and according to a 2016 estimate [1], 61,000 new cases of breast cancer are predicted
The experimental results indicate that the proposed DeepCAD system is reliable for providing aid to radiologists without the need for explicit design
DeepCAD is presented in this paper by using deep invariant features (DIFs) and along with a fourDeepCAD is presented in this paper by using deep invariant features (DIFs) and along with a layer deep neural network (DNN) multilayer architecture
Summary
Breast cancer is one of the prominent reasons for deaths of women, and according to a 2016 estimate [1], 61,000 new cases of breast cancer are predicted. If breast cancer [2] is detected earlier through mammographic screening the chances of survival are greater than 90%. The digital screening mammography [3] is extensively utilized by radiologists as the most reliable and cost-effective method. The detection or interpretation of breast masses through digital mammography [4] is a time-consuming task. The ability of mammography is limited in extremely dense breasts and detection accuracy is as low as 60%–70% [5]. In this way, the computer-aided diagnosis (CAD) systems are advanced to support radiologists for the identification of benign and malignant masses. There are many studies that suggested incorporating the CAD system [6,7,8] into the diagnostic process of screening breast images
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