GDL

Journal from gdlhub / 2021-01-04 09:43:19
Oleh : Ghada A. Shadeed, Mohammed A. Tawfeeq, Sawsan M. Mahmoud, Telkomnika
Dibuat : 2021-01-04, dengan 1 file

Keyword : chest radiography; deep learning; Internet of Things; ResNet-50; thorax diseases;
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/12997
Sumber pengambilan dokumen : web

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. By exploiting the power of the Internet of things and specific platforms to analyze a large volume of medical data, the health of a patient could be improved earlier. In this paper, the proposed model is based on pre-trained ResNet-50 for diagnosing thorax diseases. Chest x-ray images are cropped to extract the rib cage part from the chest radiographs. ResNet-50 was re-train on Chest x-ray14 dataset where a 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 the fourteen chest diseases. The results show the ability of ResNet-50 in achieving impressive performance in classifying thorax diseases.

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