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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Ezzat, Kadry Ali
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Computer Science & IT Electrical & Electronics Engineering

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Automatic liver segmentation in computed tomography scans using deep semantic segmentation Ezzat, Kadry Ali; Omran, Lamia Nabil; Seddawy, Ahmed Ibrahim Bahgat El
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4022

Abstract

Division of the liver from figured computed tomography (CT) images is fundamental for the greater part of the PC supported clinical applications, for instance, the arranging period of a liver transfer, liver volume assessment, and radiotherapy. In this paper, a programmed liver location model from clinical CT filters utilizing profound semantic division convolutional neural organization will be introduced, this model will actually want to subsequently isolate the liver utilizing CT images. The proposed model presents simultaneously the liver ID and the probabilistic division utilizing a profound convolutional neural organization. The proposed approach was endorsed on 10 CT volumes taken from open data sets 3Dircadb1. The proposed model is totally programmed with no requirement for client mediation. Quantitative results show that proposed model is reliable and exact for hepatic volume assessment in a clinical course of action with testing exactness 98.8%.
Automatic COVID-19 lung images classification system based on convolution neural network Mahdy, Lamia Nabil; Bahgat El Seddawy, Ahmed Ibrahim; Ezzat, Kadry Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5573-5579

Abstract

Coronavirus disease (COVID-19) still has disastrous effects on human life around the world. For fight that disease. Examination on the patients who have been sucked in quick and cheap way is necessary. Radiography is most effective step closer to this target. Chest X-ray is readily obtainable and cheap option. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral pneumonia from common viral pneumonia is difficult. In this study, X-ray images of 500, 500, 500, and 500 patients for healthy controls, typical viral pneumonia, bacterial pneumonia and COVID-19, were collected respectively. To our knowledge, this was the first quaternary classification study that also included classical viral pneumonia. To efficiently capture nuances in X-ray images, a new model was created by applying convolution neural network for accurate image classification. Our model outperformed to achieve an overall accuracy, sensitivity, specificity, F1-score, and area under curve (AUC) of 0.98, 0.97, 0.98, 0.97, and 0.99 respectively.
Co-Authors Bahgat El Seddawy, Ahmed Ibrahim Mahdy, Lamia Nabil Omran, Lamia Nabil Seddawy, Ahmed Ibrahim Bahgat El