SARS-CoV-2 Detection from Lung CT-Scan Images Using Fine Tuning Concept on Deep-CNN Pretrained Model

Simeon Yuda Prasetyo

doi:10.24114/cess.v8i1.40897

Authors

Simeon Yuda Prasetyo Bina Nusantara University https://orcid.org/0000-0002-6077-4003

DOI:

https://doi.org/10.24114/cess.v8i1.40897

Keywords:

SARS-CoV-2 detection, lung CT-Scan imaging, deep-CNN pretrained model, fine tuning

Abstract

The problem of the spread of COVID-19 (SARS-CoV-2) is spreading fleetly and worldwide. Beforehand discovery and opinion of complaint is veritably important to ensure the right remedy so that it needs to be enforced through various practical approaches. In former studies, complaint discovery through medical imaging has started to appear and get a good delicacy of around 80 to 90 percent using machine learning. In the deep learning era, some trials get better accuracy of 95 percent using the traditional deep learning approach. Now, deep learning has developed more fleetly, especially for image classification. therefore, it's necessary to experiment with a pretrained model approach to medical images. In addition, the fine tuning approach will also be an aspect of the approach that will be carried out in this trial to be compared and to find out its effect, specifically on CT-Scan images of the lungs for the discovery of COVID 19. The results of this experiment showed that the pretrained model approach can get high accuracy. Relatively high accuracy, the smallest testing accuracy in this trial reached 94.78 percent of the Xception without fine tuning phase, this result has beaten the machine learning approach which is didn't reach 90 percent of accuracy. The best experiment testing accuracy get 97.59 percet on the VGG 16 by applying fine tuning. The results of this trial also show that the fine tuning stage (for the top 10th layers) can increase the accuracy of the model.

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