摘要
Community-acquired pneumonia(CAP)is considered a sort of pneumonia developed outside hospitals and clinics.To diagnose community-acquired pneumonia(CAP)more efficiently,we proposed a novel neural network model.We introduce the 2-dimensional wavelet entropy(2d-WE)layer and an adaptive chaotic particle swarm optimization(ACP)algorithm to train the feed-forward neural network.The ACP uses adaptive inertia weight factor(AIWF)and Rossler attractor(RA)to improve the performance of standard particle swarm optimization.The final combined model is named WE-layer ACP-based network(WACPN),which attains a sensitivity of 91.87±1.37%,a specificity of 90.70±1.19%,a precision of 91.01±1.12%,an accuracy of 91.29±1.09%,F1 score of 91.43±1.09%,an MCC of 82.59±2.19%,and an FMI of 91.44±1.09%.The AUC of this WACPN model is 0.9577.We find that the maximum deposition level chosen as four can obtain the best result.Experiments demonstrate the effectiveness of both AIWF and RA.Finally,this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models.Our model will be distributed to the cloud computing environment.
基金
This paper is partially supported by Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)
Royal Society International Exchanges Cost Share Award,UK(RP202G0230)
British Heart Foundation Accelerator Award,UK(AA/18/3/34220)
Hope Foundation for Cancer Research,UK(RM60G0680)
Global Challenges Research Fund(GCRF),UK(P202PF11)
Sino-UK Industrial Fund,UK(RP202G0289)
LIAS Pioneering Partnerships award,UK(P202ED10)
Data Science Enhancement Fund,UK(P202RE237).
