000 | 01999 am a22002413u 4500 | ||
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042 | _adc | ||
100 | 1 | 0 |
_aWang, Shui-Hua _eauthor _92094 |
700 | 1 | 0 |
_aKhan, Muhammad Attique _eauthor _92095 |
700 | 1 | 0 |
_aZhu, Ziquan _eauthor _92096 |
700 | 1 | 0 |
_aZhang, Yu-Dong _eauthor _92097 |
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 |
_c382 _d382 |