An Enhanced Technique of COVID-19 Detection and Classification Using Deep Convolutional Neural Network from Chest X-Ray and CT Images.
Background. Coronavirus disease (COVID-19) is an infectious illness that spreads widely over a short period of time and finally causes a pandemic. Unfortunately, the lack of radiologists, improper COVID-19 diagnosing procedures, and insufficient medical supplies have all played roles in these devast...
| Published in: | BioMed Research International pp. 1 - 19 |
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| Main Authors: | , , , , |
| Format: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
12/11/2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174423747&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174423747 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/11/2023 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 174423747 174423747 174423747 10.1155/2023/6341259 174423747 ppf: 1 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Enhanced Technique of COVID-19 Detection and Classification Using Deep Convolutional Neural Network from Chest X-Ray and CT Images. aug: au: Islam, Md Khairul Rahman, Md Mahbubur Ali, Md Shahin Miah, Md Sipon Rahman, Md Habibur affil: Department of Biomedical Engineering, Islamic University, Kushtia 7003, Bangladesh sug: subj: COVID-19 Diagnosis Diagnostic Imaging Methods Technology Convolutional Neural Networks Radiography, Thoracic Tomography, X-Ray Computed Deep Learning Human COVID-19 Classification Database Management Software Models, Statistical Validity Descriptive Statistics Comparative Studies Data Management Predictive Validity Structural Equation Modeling ab: Background. Coronavirus disease (COVID-19) is an infectious illness that spreads widely over a short period of time and finally causes a pandemic. Unfortunately, the lack of radiologists, improper COVID-19 diagnosing procedures, and insufficient medical supplies have all played roles in these devastating losses of life. Deep learning (DL) could be used to detect and classify COVID-19 for potential image-based diagnosis. Materials and Methods. This paper proposes an improved deep convolutional neural network (IDConv-Net) to detect and classify COVID-19 using X-ray and computed tomography (CT) images. Before the training phase, preprocessing methods such as filtering, data normalization, classification variable encoding, and data augmentation were used in conjunction with the proposed IDConv-Net to increase the effectiveness of the detection and classification processes. To extract essential features, deep CNN is then employed. As a result, the suggested model can identify patterns and relationships crucial to the image classification task, resulting in more precise and useful diagnoses. Python and Keras (with TensorFlow as a backend) were used to carry out the experiment. Results. The proposed IDConv-Net was tested using chest X-rays and CT images collected from hospitals in Sao Paulo, Brazil, and online databases. After evaluating the model, the proposed IDConv-Net achieved an accuracy of 99.53% and 98.41% in training and testing for CT images and 97.49% and 96.99% in training and testing for X-ray images, respectively. Further, the area under the curve (AUC) value is 0.954 and 0.996 for X-ray and CT images, respectively, indicating the excellent performance of the proposed model. Conclusion. The findings of our proposed IDConv-Net model confirm that the model outperformed compared to existing COVID-19 detection and classification models. The IDConv-Net outperforms current state-of-the-art models by 2.25% for X-rays and 2.81% for CT images. Additionally, the IDConv-Net training approach is significantly quicker than the current transfer learning models. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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