000			01989 am a22002413u 4500
042			_adc
100	1	0	_aWang, Shui-Hua _eauthor
700	1	0	_aKhan, Muhammad Attique _eauthor _92095
700	1	0	_aZhu, Ziquan _eauthor _92096
700	1	0	_aZhang, Yu-Dong _eauthor
245	0	0	_aWACPN: A Neural Network for Pneumonia Diagnosis
260			_c2023.
500			_a/pmc/articles/PMC7614037/
500			_a/pubmed/36636525
520			_aCommunity-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.
540			_a
540			_ahttps://creativecommons.org/licenses/by/4.0/This work is licensed under a CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) International license.
546			_aen
690			_aArticle
655	7		_aText _2local
786	0		_nInt J Comput Syst Sci Eng
856	4	1	_uhttp://dx.doi.org/10.32604/csse.2023.031330 _zConnect to this object online.
999			_c1998 _d1998