Affiliations: [a] School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA
| [b]
University of Shanghai for Sciences and Technology, Shanghai, China
| [c] School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, USA
Correspondence:
[*]
Corresponding author: Yuchen Qiu, PhD., School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK 73019, USA. Tel.: +1 405 837 1998; E-mail: [email protected].
Abstract: PURPOSE:To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS:An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS:The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS:This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.
Keywords: Computer aided diagnosis (CAD), deep learning, breast mass classification, convolution neuron networks
