COVID-19 Diagnosis Using VGG-16 with CT Scans

Yadlapalli, Priyanka; Kora, Padmavathi; Kumari, U Aruna; Gade, Venkata Subba Reddy; Padma, T.

doi:10.1109/iconat53423.2022.9725908

Cited by 5 publications

(2 citation statements)

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“…Another research in this area was conducted by Yadlapalli et al (2022) who studied the classification of CT scans for the diagnosis of COVID-19 patients. Their approach was to use K-means clustering as an image segmentation technique to separate the area of interest from the background and then feed the segmented images to the VGG16 network and another three-layer CNN built from scratch.…”

Section: Related Workmentioning

confidence: 99%

“…Many scholars have performed studies to help improve the medical field with computer-aid techniques to help doctors diagnose diseases and plan an early treatment process for the patients because sometimes tests can take a long time to receive results ( Ayan & Ünver, 2019 ). One of these applications is using a convolutional neural networks (CNN) on medical images to extract features and train on them, for instance, using x-ray images ( Chakraborty, Murali & Mitra, 2022 ) or CT-scans ( Yadlapalli et al, 2022 ) to classify a new x-ray image as normal or diseased. As discussed earlier, diagnosing respiratory system diseases may be very useful for both doctors and patients.…”

Section: Introductionmentioning

confidence: 99%

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Detection of COVID-19 using deep learning on x-ray lung images

Odeh¹,

Alomar²,

Aljawarneh³

2022

PeerJ Computer Science

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COVID-19 is a widespread deadly virus that directly affects the human lungs. The spread of COVID-19 did not stop at humans but also reached animals, so it was necessary to limit it is spread and diagnose cases quickly by applying a quarantine to the infected people. Recently x-ray lung images are used to determine the infection and from here the idea of this research came to use deep learning techniques to analyze x-ray lung images publicly available on Kaggle to possibly detect COVID-19 infection. In this article, we have proposed a method to possibly detect the COVID-19 by analyzing the X-ray images and applying a number of deep learning pre-trained models such as InceptionV3, DenseNet121, ResNet50, and VGG16, and the results are compared to determine the best performance model and accuracy with the least loss for our dataset. Our evaluation results showed that the best performing model for our dataset is ResNet50 with accuracies of 99.99%, 99.50%, and 99.44% for training, validation, and testing respectively followed by DenseNet121, InceptionV3, and finally VGG16.

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Section: Related Workmentioning

confidence: 99%