Diagnosing thorax diseases using deep learning models
Joint Authors
Tawfiq, Muhammad A.
Mahmud, Sawsan M.
Shadid, Ghadah A.
Source
Journal of Engineering and Sustainable Development
Publisher
al-Mustansyriah University College of Engineering
Publication Date
2020-12-31
Country of Publication
Iraq
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Topics
English Abstract
Despite the availability of radiology devices in some health care centers, thorax diseases are considered as one of the most common health problems, especially in rural areas.
In this paper, pre-trained AlexNet and ResNet-50 models are used and compared for diagnosing thorax diseases.
Chest x-ray images has been used to diagnose thorax diseases and at first, the images cropped to extract the rib cage part from the chest radiographs.
In this study, the Chest x-ray14 dataset is used where chest radiograph images are inserted into the model to determine if the person is healthy or not.
In the case of an unhealthy patient, the model can classify the disease into one of fourteen chest diseases.
The results show the ability of ResNet-50 in achieving good performance with an accuracy of 92.71% in classifying thorax diseases compared with AlexNet, which has Despite the availability of radiology devices in some health care centers, thorax diseases are considered as one of the most common health problems, especially in rural areas.
In this paper, pre-trained AlexNet and ResNet-50 models are used and compared for diagnosing thorax diseases.
Chest x-ray images has been used to diagnose thorax diseases and at first, the images cropped to extract the rib cage part from the chest radiographs.
In this study, the Chest x-ray14 dataset is used where chest radiograph images are inserted into the model to determine if the person is healthy or not.
In the case of an unhealthy patient, the model can classify the disease into one of fourteen chest diseases.
The results show the ability of ResNet-50 in achieving good performance with an accuracy of 92.71% in classifying thorax diseases compared with AlexNet, which has 90.80% .
Data Type
Conference Papers
Record ID
BIM-1263603
American Psychological Association (APA)
Shadid, Ghadah A.& Tawfiq, Muhammad A.& Mahmud, Sawsan M.. 2020-12-31. Diagnosing thorax diseases using deep learning models. . Vol. 24, Special issue (2020), pp.109-115.Baghdad Iraq : al-Mustansyriah University College of Engineering.
https://search.emarefa.net/detail/BIM-1263603
Modern Language Association (MLA)
Shadid, Ghadah A.…[et al.]. Diagnosing thorax diseases using deep learning models. . Baghdad Iraq : al-Mustansyriah University College of Engineering. 2020-12-31.
https://search.emarefa.net/detail/BIM-1263603
American Medical Association (AMA)
Shadid, Ghadah A.& Tawfiq, Muhammad A.& Mahmud, Sawsan M.. Diagnosing thorax diseases using deep learning models. .
https://search.emarefa.net/detail/BIM-1263603
