12 Papers
4 Citations
Ji Li is an academic researcher from Changsha University of Science and Technology. The author has contributed to research in topics: Computer science & Radar. The author has an hindex of 4, co-authored 12 publications.
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Papers
An improved deep learning approach and its applications on colonic polyp images detection
TL;DR: A deep learning approach with global average pooling in colonoscopy for assisted diagnosis can prompt endoscopists to pay attention to polyps that may be ignored in real time, improve the detection rate, reduce missed diagnosis, and improve the efficiency of medical diagnosis.
High-Resolution Radar Target Recognition via Inception-Based VGG (IVGG) Networks.
TL;DR: The Inception module is introduced into the visual geometry group (VGG) network to make the network structure more suik / for radar target recognition and the experimental results show that the IVGG networks have better accuracies than the existing convolutional neural networks.
A SAR Image Target Recognition Approach via Novel SSF-Net Models.
TL;DR: A new convolutional neural network SSF-Net has relatively better robustness and achieves the highest recognition accuracy of 99.55% and 99.50% on SAR-SOC and SAR-EOC-1, respectively, which is 1% higher than the comparison methods.
Detecting COVID-19 in Chest X-Ray Images via MCFF-Net.
TL;DR: Wang et al. as mentioned in this paper designed the Parallel Channel Attention Feature Fusion Module (PCAF), as well as a new structure of convolutional neural network MCFF-Net proposed based on PCAF.
A Radar Signal Recognition Approach via IIF-Net Deep Learning Models.
TL;DR: Compared with other methods, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed and has higher recognition rate and better robustness under low SNR.