Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

•An end-to-end nature-inspired fine-tuning methodology for DCNN is proposed.•ChOA is used for the first time in this paper to tune the DCNN's parameters.•The proposed parallel structure causes its easy implementation on the GPUs.•The areas infected by the Covid-19 virus are detected by class ac...

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Veröffentlicht in:Expert systems with applications Jg. 213; S. 119206
Hauptverfasser: Cai, Chengfeng, Gou, Bingchen, Khishe, Mohammad, Mohammadi, Mokhtar, Rashidi, Shima, Moradpour, Reza, Mirjalili, Seyedali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.03.2023
Schlagworte:
Chest X-rays
Chimp Optimization Algorithm
Convolutional neural networks
COVID-19 diagnosis
Deep learning
Machine learning
Deep learning
COVID-19 diagnosis
Convolutional neural networks
Chest X-rays
Machine learning
Chimp Optimization Algorithm
ISSN:0957-4174, 1873-6793
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Zusammenfassung:•An end-to-end nature-inspired fine-tuning methodology for DCNN is proposed.•ChOA is used for the first time in this paper to tune the DCNN's parameters.•The proposed parallel structure causes its easy implementation on the GPUs.•The areas infected by the Covid-19 virus are detected by class activation map. Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers’ trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c-2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), andMatched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarmrate of less than 0.89%. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.119206