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.

In recent years, radiography systems have become more used in medical fields, where they are used for diagnosing many diseases. The size of the radiographs differs, as well as the size of the body parts for each patient. So many researchers crop the radiographs manually to facilitate the diagnosis and make it more reliable. Currently, the trend toward deep learning was commended where the deep learning proved its effectiveness in many fields, especially in the medical field, in which it achieves good results in diagnosing the most types of diseases. Deep learning performance increases significantly when the training process is focused on the region of interest. In this paper, segmentation is implemented by used deep learning model on the thoracic region of the radiograph in order to be cropped later. The proposed model provided automatic cropping of the radiographs where a semantic segmentation network is provided by Vgg19 model. A comparison is done with semantic segmentation network provided by Vgg16. The segmentation based on Vgg19 model outperforms Vgg16 model in cropping Chest x-ray images dataset automatically and quickly.

No abstract

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.

No abstract