At present, human action recognition is a challenging and complex task in the field of computer vision. The combination of CNN and RNN is a common and effective network structure for this task. Especially, we use 3DCNN in CNN part and ConvLSTM in RNN part. We divide the video into multiple temporal segments by average and compress each segment into one feature map by pooling layer. Adding the pooling layer, dropout layer, and batch normalization layer into ConvLSTM is our groundbreaking work. We test our model on KTH, UCF-11, and HMDB51 datasets and achieve a high accuracy of action recognition.
Lung cancer is mainly caused by malignant lung nodules. Early detection and diagnosis of lung nodules can diagnose the disease in time and significantly improve the survival rate of the patients. With the rapid development of deep learning networks in the field of medical aid diagnosis, many deep networks have been applied to lung nodule detection. Statistical distribution shows that most of the lung nodule radii are too small to be well detected. Therefore, 3D feature pyramid network (FPN) for single‐stage pulmonary nodule detection is proposed to solve this problem by combining the 3D characteristics of computed tomography (CT) image data. In addition, the squeeze‐and‐excitation (SE)‐attention module is added to improve detection performance. The validity of the network is verified on the public pulmonary nodule dataset LUNA16. The competition performance metric (CPM) value reaches 0.8934. Compared with other pulmonary nodule detection networks, the detection performance of this network improved by 2%.
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