I. Covid-19 Detection using Deep Learning Models

Shadeed, Ghada A.; Jabber, Abdullah A.; Alwash, Nahedh H.

doi:10.1109/bicits51482.2021.9509874

Cited by 4 publications

(1 citation statement)

References 18 publications

(17 reference statements)

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“…As mentioned in section 4, the X-ray images dataset is collected from two online available datasets [26], [27]. Table 8 shows the performance accuracy of our proposed DCNNs with other models such as that is suggested [29]. Table 7.…”

Section: X-ray Images Resultsmentioning

confidence: 99%

Chest Radiographs Images Retrieval Using Deep Learning Networks

Mahmoud

Al-Jubouri²,

Abdoulabbas³

2022

Bulletin EEI

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Chest diseases are among the most common diseases today. More than one million people with pneumonia enter the hospital, and about 50,000 people die annually in the U.S. alone. Also, Coronavirus disease (COVID-19) is a risky disease that threatens the health by affecting the lungs of many people around the world. Chest X-ray and CT-scan images are the radiological imaging that can be helpful to detect COVID-19. A radiologist would need to compare a patient's image with the most similar images. Content-based image retrieval in terms of medical images offers such a facility based on visual feature descriptor and similarity measurements. In this paper, a retrieval algorithm was developed to tackle such challenges based on deep convolutional neural networks (e.g., ResNet-50, AlexNet, and GoogleNet) to produce an effective feature descriptor. Also, similarity measures such as City block and Cosine were employed to compare two images. Chest X-ray and CT-scan datasets used to evaluate the proposed algorithms with a highest performance applying ResNet -50 (99% COVID-19 (+) and 98% COVID-19 (–)) and GoogleNet (84% COVID-19 (+) and 81% COVID-19 (–)) for X-ray and CT-scan respectively. The percentage increased about 1-4% when voting was used by a k-nearest neighbor classifier

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Section: X-ray Images Resultsmentioning

confidence: 99%