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Title |
Deep Learning Convolutional Network for Detection of COVID-19 from Images |
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Author |
Laheeb M. Ibrahim, Azween Abdulla, Mahadevan Supramaniam |
| Citation |
Vol. 25 No. 1 pp. 125-134 |
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Abstract |
Currently, Covid-19 disease is considered as an infectious disease that spreads very quickly in society and needs effective and fast methods to detect disease. Whereas, the best way to detect disease is to use X-rays, and at the same time, the use of chest X-rays in detecting diseases is one of the biggest challenges. Whereas dealing with chest X-rays requires a special approach to extract the properties, a deep learning mechanism has been used because of its effective characteristics in extracting properties from X-rays and provides a promising solution by transferring knowledge from the tasks of identifying general objects to the tasks of the field. In this paper, a deep learning Convolution Neural Network (CNN) model with two architectures LeNet-5 and VGG-11 uses for Covid-19 diagnoses from chest X-ray images. The experimental findings demonstrated CNN's ability to detect COVID-19 cases from a robust Chest x-ray image from the Kaggle dataset. Good accuracy of 99.17% and 98.01% using VGG-11 and LeNet-5 was achieved in the detection of Covied-19. |
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Keywords |
Convolution neural network (CNN), LeNet-5 and VGG-11, Chest X- ray, Covid-19 |
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