Abstract
In this stage of pulmonary nodules image processing, the pulmonary nodule detection and recognition based on 3D CNN has achieved great success in medical image processing. The tradition pulmonary nodule detection model has a low detection accuracy, in order to more accurately detect the pathological pulmonary nodules, this paper proposes a novel 3D CNN model based on multiscale architecture with channel and spatial attention. That is to say, multi-scale feature extraction module and attention mechanism are added to some residual blocks in 3D Fast-R-CNN, which can improve the performance of the whole network and better learn the features of candidate nodes; Secondly, in the part of regional proposal network, U-Net network and Fast RCNN are combined. The method on LUNA16 dataset shows that the average free-response receiver operating characteristic (FROC) score is 0.848 which is better than other methods. The proposed method can well reduce false positives and improve detection accuracy, providing a reference for clinical medicine.
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Zhao, Y., Wang, J., Wang, X., Wan, H. (2023). A New Pulmonary Nodule Detection Based on Multiscale Convolutional Neural Network with Channel and Attention Mechanism. In: Sun, J., Wang, Y., Huo, M., Xu, L. (eds) Signal and Information Processing, Networking and Computers. Lecture Notes in Electrical Engineering, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-19-3387-5_120
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DOI: https://doi.org/10.1007/978-981-19-3387-5_120
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