Deep Learning-and Transfer Learning-based Models for COVID-19 Detection using Radiography Images
2023 International Conference on Advances in Electronics, Control and Communication Systems, ICAECCS 2023
; 2023.
Article
in English
| Scopus | ID: covidwho-2324821
ABSTRACT
Image classification and segmentation techniques are still very popular in the medical field (for healthcare), in which the medical image plays an important role in the detection and screening of diseases. Recently, the spread of new viral diseases, namely Covid-19, requires powerful computer models and rich resources (datasets) to fight this phenomenon. In this study, we propose to examine the CNN Deep Learning algorithm and two Transfer Learning models, namely RestNet50 and MobileNetV2 using the pretrained model of the ImageNet database, experimented on the new dataset (COVID-QU-Ex Dataset 2022) offered by the University of Qatar. These models are tested to classify radiography images into two classes (Covid19 and Normal). The results achieved by CNN (Acc =95.97%), ResNet50 (Acc =95.53%) and MobileNetV2 (Acc =97.32%) show that these algorithms are promising in order to combat this Covid-19 disease by detecting it through thoracic images (Chest X-ray type). © 2023 IEEE.
Chest Xray; CNN; Covid-19 detection; Image classification; ImageNet; MobileNetV2; Radiography images; ResNet50; Deep learning; Diagnosis; Image segmentation; Learning algorithms; Learning systems; Medical imaging; X ray radiography; Chest x-rays; Images classification; Images segmentations; Learning Based Models; Transfer learning
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Diagnostic study
Language:
English
Journal:
2023 International Conference on Advances in Electronics, Control and Communication Systems, ICAECCS 2023
Year:
2023
Document Type:
Article
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