TY - JOUR ID - TI - DIAGNOSING THORAX DISEASES USING DEEP LEARNING MODELS AU - Sawsan M. Mahmoud AU - Mohammed A. Tawfeeq AU - Ghada A. Shadeed PY - VL - IS - Conference proceedings 2020 SP - 109 EP - 115 JO - Journal of Engineering and Sustainable Development (JEASD) مجلة الهندسة والتنمية المستدامة SN - 25200917 25200925 AB - 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%.

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