Abstract
The classification of benign and malignant masses in mammograms by Computer-Aided Diagnosis (CAD) is one of the most difficult and important tasks in the development of CAD systems. This classification has commonly been automated by extracting a set of handcrafted features from mammograms and relating the responses to breast cancer. Recently, the application of Deep Learning (DL) technology in medical imaging informatics has been attracting extensive research interest. However, limited medical image datasets and feature expression often reduce the performance of DL-based schemes. Therefore, this study aims to develop a new combined feature CAD method based on DL for classifying mammographic masses into three classes: normal, benign and cancer (malignant) masses. Three kinds of breast masses were scored by using Deep Convolution Neural Network (DCNN) as a feature extractor. Then the scoring features are combined with the image texture features as input to the classifier. This features including the scoring features, Gray-Level Co-occurrence Matrix (GLCM) and Histogram of Oriented Gradient (HOT) were employed to extract the breast mass information in mammograms and the classifier of Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were trained for the classification task. Accuracy (ACC), Precision (Pre), Recall (Rec), F1-score (F1), and Overall Accuracy (Overall ACC) are used to evaluate the performance of the proposed system and the results show that the proposed multi-features combination model performs the best results. The performance of the XGBoost classifier has proved to be better in comparison to the SVM classification algorithms. As a result, when XGBoost was used as a classifier, the correct identification rate of the Overall ACC was 92.80% and that of malignant tumors was 84%, with reasonable and best results. These results indicate that the proposed method may help in more accurately diagnosing cases that are difficult to classify on images.
Highlights
Breast cancer is a huge health threat [1], presenting an increasing incidence and mortality rate in all age groups in the past decades [2]
When the epoch was 100000, i.e., Figure 4 (c), Deep Convolution Neural Network (DCNN)-regions of interest (ROIs)-TL was significantly better than DCNN ROIs in terms of the classification loss
To elevate the classification performance of the networks, in this study, we proposed a method to classify breast masses into benign, cancer and normal in mammography by using multi-feature and combine the classification results based on DCNN as features
Summary
Breast cancer is a huge health threat [1], presenting an increasing incidence and mortality rate in all age groups in the past decades [2]. It is one of the most common causes of cancer deaths in women worldwide and it is responsible for 23% of all cancer cases and 14% of cancer-related deaths amongst women [3]. The wide use of mammography technology can early detect the occult breast cancer in asymptomatic women, which greatly promotes this favorable trend of effectively reducing mortality [2].
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