Because of the unclear boundaries and different shapes and sizes of breast masses, the accuracy of using traditional computer-aided diagnosis systems is low and it is difficult to meet the clinical requirements of physicians. In this paper, we propose a breast mass detection algorithm based on the combination of YOLOv5 and improved coordinate attention, to meet the clinical requirements of high accuracy and real-time. First, a novel backbone feature extraction network is constructed by combining the underlying backbone network and attention mechanism to fully learn useful features and suppress irrelevant features, thus enhancing the feature expression capability. Then a multi-path aggregation network is designed as the neck of feature fusion to fully fuse the feature information at different levels. Validation experiments are conducted on the DDSM breast mass dataset, and the results show that the network can accurately detect masses of different scales in different backgrounds with better real-time performance. Compared with the base YOLOv5, the network improves by 2.3% in accuracy.
Lumbar vertebral fracture seriously endangers the health of people, which has a higher mortality. Due to the tiny difference among various fracture features in CT images, multiple vertebral fractures classification has a great challenge for computer-aided diagnosis system. To solve this problem, this paper proposes a multiclass PSVM ensemble method with multi-feature selection to recognize lumbar vertebral fractures from spine CT images. In the proposed method, firstly, the active contour model is utilized to segment lumbar vertebral bodies. It is helpful for the subsequent feature extraction. Secondly, different image features are extracted, including 3 geometric shape features, 3 texture features, and 5 height ratios. The importance of these features is analyzed and ranked by using infinite feature selection method, thus selecting different feature subsets. Finally, three multiclass probability SVMs with binary tree structure are trained on three datasets. The weighted voting strategy is used for the final decision fusion. To validate the effectiveness of the proposed method, probability SVM, K-nearest neighbor, and decision tree as base classifiers are compared with or without feature selection. Experimental results on 25 spine CT volumes demonstrate that the advantage of the proposed method compared to other classifiers, both in terms of the classification accuracy and Cohen’s kappa coefficient.
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