Tao Zhou1,3, Yujie Guo1,3,*, Caiyue Peng1,3, Yuxia Niu1,3, Yunfeng Pan1,3, Huiling Lu2
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4863-4882, 2024, DOI:10.32604/cmc.2024.050767
Abstract Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot. However, there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images. A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper. The main innovations are as follows: Firstly, the Multi-scale Residual Feature Extraction Module (MRFEM) is designed to effectively extract multi-scale features. The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively. Secondly, the… More >
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