Loxapine Succinate br The other challenging problems of breast
In general, the training process for this method consists of two stages. In the first stage, we aimed at building a set of deep convolutional neural networks trained to extract the most useful visual features from multi-scale training images. Recent research demonstrated that an ensemble of DCNNs can perform better than a single DCNN [33,34]. Hence, we trained independently multiple DCNNs with different input scales and combined their final feature vectors by using a new boosting strategy. In fact, these advance feature vectors were used as inputs to gradient boosting trees classifiers . In the second training stage, each gradient boosting trees classifier was trained based on the new dataset of deep feature vectors extracted from the corresponding DCNN in the first stage. After the training processes, each boosting trees classifier can achieve a better accuracy rate than its corresponding DCNN classifier. Finally, we applied the majority voting strategy for combining the boosting trees classifiers. This combination resulted in a final boosting classifier that achieved the best classification performance thanks to the improved extraction of multi-scale deep features of breast cancer tumors.
In this study, we tested our proposed algorithms and its competitors to evaluate the accuracy of breast detection in a challenging dataset. Based on extensive experiments, our algorithms are shown to significantly outperform state-of-the-art algorithms. In particular, our proposed method provides several contributions:
• First, we present an ensemble of DCNNs trained to extract the most useful visual features from multi-scale training-images. By building the ensemble of DCNNs, both global and local features of breast cancer tumors can be extracted properly, and the accuracy of multi-label breast cancer detection is thus significantly improved.
• Second, we propose to use the gradient boosting trees algorithm to boost the classification performance of the DCNN classifiers. We proved that the combination of DCNN and a boosting trees classifier leads to a better classification per-formance despite the limited number of breast cancer samples and imbalanced training data.