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Computerized determination scheme for histological classification of masses on breast ultrasonographic images using combination of CNN features and morphologic features

It can be difficult for clinicians to correctly determine biopsy or follow-up for masses on breast ultrasonographic images. The purpose of this study was to develop a computerized determination scheme for histological classification of masses using a combination of CNN (convolutional neural network) features and morphologic features. The database consisted of 585 breast ultrasonographic images. It included 288 malignant masses (218 invasive carcinomas and 70 noninvasive carcinomas) and 297 benign masses (182 fibroadenomas and 115 cysts). In the proposed method, CNN features and morphologic features were first determined from a mass. The CNN features were defined by reducing the dimensionality of the output of the final pooling layer in GoogLeNet using a principal component analysis. The morphologic features were also defined by taking into account image features commonly used for describing masses on breast ultrasonographic images. A support vector machine (SVM) with the CNN features and the morphologic features was employed to classify among histological classifications of masses. Three-fold cross validation method was used for training and testing the GoogLeNet and the SVM. The classification accuracies with the proposed method were 84.4% (184/218) for invasive carcinomas, 72.9% (51/70) for noninvasive carcinomas, 85.7% (156/182) for fibroadenomas, and 87.8% (101/115) for cysts, respectively. The sensitivity and the specificity were 87.2% (251/288) and 93.3% (277/297), whereas the positive predictive value and the negative predictive value were 92.6% (251/271) and 88.2% (277/314). The proposed method yielding high classification accuracies would be useful in the differential diagnosis of masses on ultrasonographic images as diagnosis aid.