Classification of COVID19 X-ray Images Based on Transfer Learning InceptionV3 Deep Learning Model

Amin, Hassan; Darwish, Ashraf; Hassanien, Aboul Ella

doi:10.1007/978-3-030-63307-3_7

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…The authors of [ 17 ] suggest a three-class (namely: normal, pneumonia, and COVID-19) classification model to detect and classify X-ray images. A transfer learning InceptionV3 model is used.…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Diagnosis and Classification Using Radiological Imaging and Deep Learning Techniques: A Comparative Study

et al. 2022

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In December 2019, the novel coronavirus disease 2019 (COVID-19) appeared. Being highly contagious and with no effective treatment available, the only solution was to detect and isolate infected patients to further break the chain of infection. The shortage of test kits and other drawbacks of lab tests motivated researchers to build an automated diagnosis system using chest X-rays and CT scanning. The reviewed works in this study use AI coupled with the radiological image processing of raw chest X-rays and CT images to train various CNN models. They use transfer learning and numerous types of binary and multi-class classifications. The models are trained and validated on several datasets, the attributes of which are also discussed. The obtained results of various algorithms are later compared using performance metrics such as accuracy, F1 score, and AUC. Major challenges faced in this research domain are the limited availability of COVID image data and the high accuracy of the prediction of the severity of patients using deep learning compared to well-known methods of COVID-19 detection such as PCR tests. These automated detection systems using CXR technology are reliable enough to help radiologists in the initial screening and in the immediate diagnosis of infected individuals. They are preferred because of their low cost, availability, and fast results.

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Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Diagnosis and Classification Using Radiological Imaging and Deep Learning Techniques: A Comparative Study

et al. 2022

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“…The classification achieved a final accuracy of 97.67%. Musha et al [ 63 ], Canario et al [ 64 ], El-Dahshan et al [ 65 ], and Amin et al [ 66 ] all used CT and CXR images in deep learning models to diagnose COVID-19 infection. These studies obtained accuracies of 97.9%, 98%, 99%, 99.3%, and 96.6%, respectively.…”

Section: Ai For Covid-19 Diagnosismentioning

confidence: 99%

“…Studies that used deep learning on either X-ray or CT images (or both) to detect COVID-19 include Panwar et al [ 76 ], Gilanie et al [ 60 ], and Amin et al [ 66 ], etc. DL was used alongside thoracic CT images to create a complete AI system that detects COVID-19 and measures severity and disease progression [ 77 ].…”

Section: Ai For Covid-19 Diagnosismentioning

confidence: 99%

The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis

2022

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Artificial intelligence (AI) has been shown to solve several issues affecting COVID-19 diagnosis. This systematic review research explores the impact of AI in early COVID-19 screening, detection, and diagnosis. A comprehensive survey of AI in the COVID-19 literature, mainly in the context of screening and diagnosis, was observed by applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Data sources for the years 2020, 2021, and 2022 were retrieved from google scholar, web of science, Scopus, and PubMed, with target keywords relating to AI in COVID-19 screening and diagnosis. After a comprehensive review of these studies, the results found that AI contributed immensely to improving COVID-19 screening and diagnosis. Some proposed AI models were shown to have comparable (sometimes even better) clinical decision outcomes, compared to experienced radiologists in the screening/diagnosing of COVID-19. Additionally, AI has the capacity to reduce physician work burdens and fatigue and reduce the problems of several false positives, associated with the RT-PCR test (with lower sensitivity of 60–70%) and medical imaging analysis. Even though AI was found to be timesaving and cost-effective, with less clinical errors, it works optimally under the supervision of a physician or other specialists.

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Transfer Learning for COVID-19 Detection in Medical Images

Azhari

2024

SN COMPUT. SCI.

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As of late, the Covid infection 2019 (COVID-19) has caused a pandemic sickness in more than 200 nations, therefore impacting billions of people. To control the spread of the coronavirus, it is crucial to detect infected individuals and ensure their complete isolation to prevent further infection. Chest X-rays and CT-scans have been proven to be very promising as signals of the infection can be clearly shown in lung areas. Transfer learning from ImageNet dataset has become the latent trend in medical imaging applications. However, there are major differences between ImageNet and medical imaging datasets. Therefore, the feasibility of transfer learning in medical applications remains questionable. This paper investigates the performance of five fine-tuned pre-trained models for chest x-rays and CT-scans classification in contrast with a deep CNN model built from scratch. DenseNet121, Resnet-50, Inception v2, Resnet101-V2 and VGG16 are selected and initialized with either random or pre-trained weights to classify augmented images into two classes: Covid and non-Covid. The performance evaluation proves the minuscule impact of training transfer learning models for good quality results, as all CNN models contribute almost equally to the classification and achieve considerable results in terms of precision, accuracy, recall and F1 score.

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Classification of COVID19 X-ray Images Based on Transfer Learning InceptionV3 Deep Learning Model

Cited by 6 publications

References 11 publications

COVID-19 Diagnosis and Classification Using Radiological Imaging and Deep Learning Techniques: A Comparative Study

COVID-19 Diagnosis and Classification Using Radiological Imaging and Deep Learning Techniques: A Comparative Study

The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis

Transfer Learning for COVID-19 Detection in Medical Images

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