Diagnosing Thorax Diseases Using Deep Learning Models

Shadeed, Ghada A.; Tawfeeq, Mohammed Ali; Mahmoud, Sawsan M.

doi:10.31272/jeasd.conf.1.12

Cited by 4 publications

(2 citation statements)

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“…This means that GoogleNet feature need less time than two other and it is better to use. Moreover, we can judge the significant difference between GoogleNet and other two models by using a t-test statistical method [33] as (8).…”

Section: Ct-scan Images Resultsmentioning

confidence: 99%

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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: Ct-scan Images Resultsmentioning

confidence: 99%

“…Different DCNNs models have been used in diagnosing chest diseases before COVID-19 appeared recently [4]- [8]. For example, in Elshennawy and Ibrahim [4] four different models have been applied: ResNet 152V2, MobileNet V2 as pre-trained models, a kind of convolutional neural network (CNN) and a long short term memory (LSTM) model.…”

Section: Related Workmentioning

confidence: 99%

Chest Radiographs Images Retrieval Using Deep Learning Networks

Mahmoud

Al-Jubouri²,

Abdoulabbas³

2022

Bulletin EEI

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Epilepsy Database Collection Implementing Smartphone Inertial Sensors

Ibrahim,

Makki

2023

2023 Second International Conference on Advanced Computer Applications (ACA)

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I. Covid-19 Detection using Deep Learning Models

Shadeed

Jabber

Alwash

2021

2021 1st Babylon International Conference on Information Technology and Science (BICITS)

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The outbreak of Corona disease, or the so-called Covid-19, has affected the course of human life. Detecting this disease early reduces the risk of spreading the disease. Thus, get rid of this epidemic sooner. In this paper, a system is created that helps to identify and detect Covid-19 disease through X-ray radiation. GoogLeNet, ResNet-101, Inception v3 network, and DAG3Net that are used for comparison purposes. Good results have been obtained in detecting Covid-19 disease, where the DAG3Net produces diagnostic (validation, training, testing and overall) accuracies of (96.15%, 94.34%, 96.75% and 96.58%) respectively, while the GoogLeNet, ResNet-101, and Inception v3 network are produced (98.08%, 100%, 99.59% and 99.72%) respectively.

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Diagnosing Thorax Diseases Using Deep Learning Models

Cited by 4 publications

References 0 publications

Chest Radiographs Images Retrieval Using Deep Learning Networks

Chest Radiographs Images Retrieval Using Deep Learning Networks

Epilepsy Database Collection Implementing Smartphone Inertial Sensors

I. Covid-19 Detection using Deep Learning Models

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