tetano
Editor, Senior Moderator
Heliyon
. 2024 Feb 29;10(5):e26938.
doi: 10.1016/j.heliyon.2024.e26938. eCollection 2024 Mar 15. A Hybrid Deep Learning CNN model for COVID-19 detection from chest X-rays
Mohan Abdullah[SUP] 1 [/SUP], Ftsum Berhe Abrha[SUP] 1 [/SUP], Beshir Kedir[SUP] 1 [/SUP], Takore Tamirat Tagesse[SUP] 1 [/SUP]
Affiliations
Coronavirus disease (COVID-2019) is emerging in Wuhan, China in 2019. It has spread throughout the world since the year 2020. Millions of people were affected and caused death to them till now. To avoid the spreading of COVID-2019, various precautions and restrictions have been taken by all nations. At the same time, infected persons are needed to identify and isolate, and medical treatment should be provided to them. Due to a deficient number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, a Chest X-ray image is becoming an effective technique for diagnosing COVID-19. In this work, the Hybrid Deep Learning CNN model is proposed for the diagnosis COVID-19 using chest X-rays. The proposed model consists of a heading model and a base model. The base model utilizes two pre-trained deep learning structures such as VGG16 and VGG19. The feature dimensions from these pre-trained models are reduced by incorporating different pooling layers, such as max and average. In the heading part, dense layers of size three with different activation functions are also added. A dropout layer is supplemented to avoid overfitting. The experimental analyses are conducted to identify the efficacy of the proposed hybrid deep learning with existing transfer learning architectures such as VGG16, VGG19, EfficientNetB0 and ResNet50 using a COVID-19 radiology database. Various classification techniques, such as K-Nearest Neighbor (KNN), Naive Bayes, Random Forest, Support Vector Machine (SVM), and Neural Network, were also used for the performance comparison of the proposed model. The hybrid deep learning model with average pooling layers, along with SVM-linear and neural networks, both achieved an accuracy of 92%.These proposed models can be employed to assist radiologists and physicians in avoiding misdiagnosis rates and to validate the positive COVID-19 infected cases.
Keywords: COVID- coronavirus disease 2019 (COVID-19); Hybrid Deep Learning CNN model; Neural network (NN); Visual Geometry Group-16 (VGG16); Visual Geometry Group-19 (VGG-19); X-rays.
. 2024 Feb 29;10(5):e26938.
doi: 10.1016/j.heliyon.2024.e26938. eCollection 2024 Mar 15. A Hybrid Deep Learning CNN model for COVID-19 detection from chest X-rays
Mohan Abdullah[SUP] 1 [/SUP], Ftsum Berhe Abrha[SUP] 1 [/SUP], Beshir Kedir[SUP] 1 [/SUP], Takore Tamirat Tagesse[SUP] 1 [/SUP]
Affiliations
- PMID: 38468922
- PMCID: PMC10926074
- DOI: 10.1016/j.heliyon.2024.e26938
Coronavirus disease (COVID-2019) is emerging in Wuhan, China in 2019. It has spread throughout the world since the year 2020. Millions of people were affected and caused death to them till now. To avoid the spreading of COVID-2019, various precautions and restrictions have been taken by all nations. At the same time, infected persons are needed to identify and isolate, and medical treatment should be provided to them. Due to a deficient number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, a Chest X-ray image is becoming an effective technique for diagnosing COVID-19. In this work, the Hybrid Deep Learning CNN model is proposed for the diagnosis COVID-19 using chest X-rays. The proposed model consists of a heading model and a base model. The base model utilizes two pre-trained deep learning structures such as VGG16 and VGG19. The feature dimensions from these pre-trained models are reduced by incorporating different pooling layers, such as max and average. In the heading part, dense layers of size three with different activation functions are also added. A dropout layer is supplemented to avoid overfitting. The experimental analyses are conducted to identify the efficacy of the proposed hybrid deep learning with existing transfer learning architectures such as VGG16, VGG19, EfficientNetB0 and ResNet50 using a COVID-19 radiology database. Various classification techniques, such as K-Nearest Neighbor (KNN), Naive Bayes, Random Forest, Support Vector Machine (SVM), and Neural Network, were also used for the performance comparison of the proposed model. The hybrid deep learning model with average pooling layers, along with SVM-linear and neural networks, both achieved an accuracy of 92%.These proposed models can be employed to assist radiologists and physicians in avoiding misdiagnosis rates and to validate the positive COVID-19 infected cases.
Keywords: COVID- coronavirus disease 2019 (COVID-19); Hybrid Deep Learning CNN model; Neural network (NN); Visual Geometry Group-16 (VGG16); Visual Geometry Group-19 (VGG-19); X-rays.