Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach

Polat, Özlem

doi:10.16984/saufenbilder.903886

Cited by 17 publications

(9 citation statements)

References 36 publications

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“…A total of 1,680 images reproduced through the method specified from this data set were used, 560 of which are labeled as Covid, 560 of which are labeled as Viral Pneumonia or 560 of which are labeled as Normal X-ray images. A decomposition method was applied at a rate of 70% for the training of the images and 30% for the test ( 54 ).…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images

Yenikaya,

Kerse,

Oktaysoy

2024

Front. Public Health

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PurposeArtificial intelligence has led to significant developments in the healthcare sector, as in other sectors and fields. In light of its significance, the present study delves into exploring deep learning, a branch of artificial intelligence.MethodsIn the study, deep learning networks ResNet101, AlexNet, GoogLeNet, and Xception were considered, and it was aimed to determine the success of these networks in disease diagnosis. For this purpose, a dataset of 1,680 chest X-ray images was utilized, consisting of cases of COVID-19, viral pneumonia, and individuals without these diseases. These images were obtained by employing a rotation method to generate replicated data, wherein a split of 70 and 30% was adopted for training and validation, respectively.ResultsThe analysis findings revealed that the deep learning networks were successful in classifying COVID-19, Viral Pneumonia, and Normal (disease-free) images. Moreover, an examination of the success levels revealed that the ResNet101 deep learning network was more successful than the others with a 96.32% success rate.ConclusionIn the study, it was seen that deep learning can be used in disease diagnosis and can help experts in the relevant field, ultimately contributing to healthcare organizations and the practices of country managers.

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Section: Methodsmentioning

confidence: 99%

Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images

Yenikaya,

Kerse,

Oktaysoy

2024

Front. Public Health

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“…It combines several filter sizes into a single image block rather than being limited to a single filter size, which is then pass to the following layer. [ 59 ].…”

Section: Popular Convolutional Neural Network Architectures For Covid...mentioning

confidence: 99%

“…The entry flow, the middle flow, which is repeated eight times, and the exit flow are all the steps that the data must initially go through. Keep in mind that batch normalization comes after convolution and separable convolution layer [ 59 ]…”

Section: Popular Convolutional Neural Network Architectures For Covid...mentioning

confidence: 99%

Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey

2023

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Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease.

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“…The Xception network (Polat, 2021) used in this study can be called an interpretation of the Inception modules. The name Xception also comes from "extreme inception".…”

Section: Transfer Learning and Xceptionmentioning

confidence: 99%

Re-Identification of Rats with Transfer Learning

Molares-Ulloa,

Ortega-Femia,

Dorado

et al. 2023

Congreso XoveTIC: Impulsando El Talento Científico (6º. 2023. A Coruña)

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The study of animal behavior in laboratory experiments is key in ethology, ecotoxicology, neuroscience and other fields. Although modern studies use computer imaging techniques, current solutions cannot preserve the identity of multiple individuals in social experiments. Thanks to the use of Transfer Learning we seek to overcome this limitations while maintaining the effectiveness of Deep Learning and reducing its computational times. With this technique we achieved promising results in the re-identification of rats after an occlusion process

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Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach

Cited by 17 publications

References 36 publications

Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images

Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images

Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey

Re-Identification of Rats with Transfer Learning

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