Breast cancer is the most common cancer in women and poses a great threat to women's life and health. Mammography is an effective method for the diagnosis of breast cancer, but the results are largely limited by the clinical experience of radiologists. Therefore, the main purpose of this study is to perform two-stage classification (Normal/Abnormal and Benign/ Malignancy) of two-view mammograms through convolutional neural network. In this study, we constructed a multi-view feature fusion network model for classification of mammograms from two views, and we proposed a multi-scale attention DenseNet as the backbone network for feature extraction. The model consists of two independent branches, which are used to extract the features of two mammograms from different views. Our work mainly focuses on the construction of multi-scale convolution module and attention module. The final experimental results show that the model has achieved good performance in both classification tasks. We used the DDSM database to evaluate the proposed method. The accuracy, sensitivity and AUC values of normal and abnormal mammograms classification were 94.92%, 96.52% and 94.72%, respectively. And the accuracy, sensitivity and AUC values of benign and malignant mammograms classification were 95.24%, 96.11% and 95.03%, respectively.
Breast cancer is the cancer with the highest incidence in women, and early detection can effectively improve the survival rate of patients. Mammography is an important method for physicians to screening breast cancer, but the diagnosis of mammograms by physicians depends largely on clinical practice experience. Studies have shown that using computer-aided diagnosis techniques can help doctors diagnose breast cancer. Methods: In this paper, the method of convolutional neural network is mainly used to classify benign and malignant breast masses in the mammograms. First, we use multi-scale residual networks and densely connected networks as backbone networks to extract the features of global image patches and local image patches. Second, we use the attention module named convolutional block attention module (CBAM) to improve the two feature extraction networks to enhance the network's feature expression ability. Finally, we fuse the features of multi-scale image patches to achieve the classification of benign and malignant breast masses. Results: In the digital database for screening mammography (DDSM) database, the accuracy, sensitivity, AUC value and corresponding standard deviation of our method are 0.9626 AE 0.0110, 0.9719 AE 0.0126, and 0.9576 AE 0.0064, respectively. Compared with the commonly used ResNet (AUC = 0.8823 AE 0.0112) and DenseNet (AUC = 0.9141 AE 0.0085), the performance of our method has improved. In addition, we also used the INbreast database to train and validate the proposed method. The accuracy, sensitivity, AUC and corresponding standard deviations are 0.9554 AE 0.0296, 0.9605 AE 0.0228, and 0.9468 AE 0.0085, respectively. Conclusions: Compared with the previous work, our proposed method uses multi-scale image features, has better classification performance in breast mass patches classification tasks, and can effectively assist physicians in breast cancer diagnosis.
Breast cancer is the second deadliest cancer among women. Mammography is an important method for physicians to diagnose breast cancer. The main purpose of this study is to use deep learning to automatically classify breast masses in mammograms into benign and malignant. This study proposes a two‐view mammograms classification model consisting of convolutional neural network (CNN) and recurrent neural network (RNN), which is used to classify benign and malignant breast masses. The model is composed of two branch networks, and two modified ResNet are used to extract breast‐mass features of mammograms from craniocaudal (CC) view and mediolateral oblique (MLO) view, respectively. In order to effectively utilise the spatial relationship of the two‐view mammograms, gate recurrent unit (GRU) structures of RNN is used to fuse the features of the breast mass from the two‐view. The digital database for screening mammography (DDSM) be used for training and testing our model. The experimental results show that the classification accuracy, recall and area under curve (AUC) of our method reach 0.947, 0.941 and 0.968, respectively. Compared with previous studies, our method has significantly improved the performance of benign and malignant classification.
With the computer image processing and technology development, vision sensors in mobile robot navigation and obstacle recognition was paid more and more attention. In this paper Adaboost algorithm is used to identify obstacles of intelligent wheelchair in Visual c + +6.0 platforms. With the AdaBoost algorithm training strong classifier for obstacle detection, then use the classifier to detect the target obstacle. Fuzzy neural network is used to fusion sonar information and visual information of wheelchair make the obstacle avoidance path of the wheelchair to be more intelligent and optimization.
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