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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Sawsan M. Mahmoud
Mustansiriyah University
Computer Science & IT Electrical & Electronics Engineering

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Deep learning model for thorax diseases detection Ghada A. Shadeed; Mohammed A. Tawfeeq; Sawsan M. Mahmoud
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.12997

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. 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.
Overlapped hierarchical clusters routing protocol for improving quality of service Hayder Fakher Jassim; Mohammed A. Tawfeeq; Sawsan M. Mahmoud
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 3: June 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i3.18354

Abstract

The rapid development in communications and sensors technologies make wireless sensor networks (WSNs) as essential key in several advanced applications such as internet of things (IoT). The increasing demands on using WSNs required high quality of services (QoS) because most WSNs applications have critical requirements. This work aims to offer a routing protocol to improve the QoS in WSNs, taking in consideration its ability to prolong the lifetime of the network, optimize the utilization of the limited bandwidth available, and decrease the latency that accompanies the packets transmitted to the gateway. The proposed protocol is called overlapped hierarchical cluster routing protocol (OHCRP). OHCRP is compared with the traditional routing protocols such as SPEED, and THVR. The results show that OHCRP reduces latency effectively and achieve high energy conservation, which lead to increase the network lifetime and insure network availability.
Chest radiographs images retrieval using deep learning networks Sawsan M. Mahmoud; Hanan A. S. Al-Jubouri; Tawfeeq E. Abdoulabbas
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3478

Abstract

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
Co-Authors Ghada A. Shadeed Hanan A. S. Al-Jubouri Hayder Fakher Jassim Mohammed A. Tawfeeq Tawfeeq E. Abdoulabbas