COVID-19 Detection via a 6-Layer Deep Convolutional Neural Network

Hou, Shouming; Han, Ji

doi:10.32604/cmes.2022.016621

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…This study compares the proposed ELUCNN model with SOTA COVID-19 diagnosis models on this entire 640-image dataset using ten runs of tenfold CV. The 14 comparison models comprise K-ELM (Yang 2018 ), CNN-SP (Zhang 2022a ), COVNet (Li et al 2020 ), DLA (Ni et al 2020 ), WSF (Wang et al 2020 ), DC-Net (Zhang 2022b ), WRE (Wu 2020 ), FSVC (El-kenawy et al 2020 ), GLCM (Chen 2020 ), 6l-DCNN (Hou 2022 ), PZM (Khan 2021 ), Jaya (Wang 2021 ), SNN (Pi 2021 ), and DLM (Gafoor et al 2022 ). Note here K-ELM (Yang 2018 ) is originally developed for brain detection.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

et al. 2023

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COVID-19 is a positive-sense single-stranded RNA virus caused by a strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several noteworthy variants of SARS-CoV-2 were declared by WHO as Alpha, Beta, Gamma, Delta, and Omicron. Till 13/Dec/2022, it has caused 6.65 million death tolls, and over 649 million confirmed positive cases. Based on the convolutional neural network (CNN), this study first proposes a ten-layer CNN as the backbone model. Then, the exponential linear unit (ELU) is introduced to replace ReLU, and the traditional convolutional block is now transformed into conv-ELU. Finally, an ELU-based CNN (ELUCNN) model is proposed for COVID-19 diagnosis. Besides, the MDA strategy is used to enhance the size of the training set. We develop a mobile app integrating ELUCNN, and this web app is run on a client–server modeled structure. Ten runs of the tenfold cross-validation experiment show our model yields a sensitivity of $$94.41\pm 0.98$$ 94.41 ± 0.98 , a specificity of $$94.84\pm 1.21$$ 94.84 ± 1.21 , an accuracy of $$94.62\pm 0.96$$ 94.62 ± 0.96 , and an F1 score of $$94.61\pm 0.95$$ 94.61 ± 0.95 . The ELUCNN model and mobile app are effective in COVID-19 diagnosis and give better results than 14 state-of-the-art COVID-19 diagnosis models concerning accuracy.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…In Chen ( 2020 ) ‘s paper, the authors mixed a gray-level co-occurrence matrix (GLCM) with a support vector machine (SVM). Hou ( 2022 ) proposed a 6-layer deep convolutional neural network. The name of their method is shortened to 6 l-DCNN.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

et al. 2023

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“…Chouhan et al [26] proposed a new CNN model for pneumonia diagnosis based on transfer learning, which improves diagnosis accuracy by integrating multiple pre-training models. Hou et al [27] proposed a six-layer convolutional neural network combining maximum pooling, batch normalization, and an Adam algorithm that outperforms several state-of-the-art methods with a 10-weight cross-validation method. In this research, the FG-CPD is developed for the children's pneumonia diagnosis.…”

Section: Related Work 21 Convolutional Neural Network Based Pneumonia...mentioning

confidence: 99%

A New Childhood Pneumonia Diagnosis Method Based on Fine-Grained Convolutional Neural Network

Zhang¹,

Qiu²,

Zhu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Pneumonia is part of the main diseases causing the death of children. It is generally diagnosed through chest Xray images. With the development of Deep Learning (DL), the diagnosis of pneumonia based on DL has received extensive attention. However, due to the small difference between pneumonia and normal images, the performance of DL methods could be improved. This research proposes a new fine-grained Convolutional Neural Network (CNN) for children's pneumonia diagnosis (FG-CPD). Firstly, the fine-grained CNN classification which can handle the slight difference in images is investigated. To obtain the raw images from the real-world chest X-ray data, the YOLOv4 algorithm is trained to detect and position the chest part in the raw images. Secondly, a novel attention network is proposed, named SGNet, which integrates the spatial information and channel information of the images to locate the discriminative parts in the chest image for expanding the difference between pneumonia and normal images. Thirdly, the automatic data augmentation method is adopted to increase the diversity of the images and avoid the overfitting of FG-CPD. The FG-CPD has been tested on the public Chest X-ray 2017 dataset, and the results show that it has achieved great effect. Then, the FG-CPD is tested on the real chest X-ray images from children aged 3-12 years ago from Tongji Hospital. The results show that FG-CPD has achieved up to 96.91% accuracy, which can validate the potential of the FG-CPD.

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“…Some studies have also used models based on classical deep-learning methods to diagnose COVID-19. Hou and Han (2022) (2021) proposed a multi-input deep convolutional attention network constructed using a convolutional block attention module. Their model has achieved more than 90% accuracy.…”

Section: Introductionmentioning

confidence: 99%

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

Wang¹,

Pei²,

WANG³

et al. 2023

Biocell

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Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65% ± 1.86%, a specificity of 94.32% ± 2.07%, a precision of 94.30% ± 2.04%, an accuracy of 93.99% ± 1.78%, an F1-score of 93.97% ± 1.78%, Matthews Correlation Coefficient of 87.99% ± 3.56%, and Fowlkes-Mallows Index of 93.97% ± 1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.

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COVID-19 Detection via a 6-Layer Deep Convolutional Neural Network

Cited by 8 publications

References 34 publications

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

A New Childhood Pneumonia Diagnosis Method Based on Fine-Grained Convolutional Neural Network

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

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