Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Ahmed Ibrahim Bahgat El Seddawy
Arab Academy for Science and Technology and Maritime Transport
Computer Science & IT Electrical & Electronics Engineering Engineering

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Automatic COVID-19 lung images classification system based on convolution neural network Lamia Nabil Mahdy; Ahmed Ibrahim Bahgat El Seddawy; Kadry Ali Ezzat
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.
Corona destroyer based ultra violet sanitizing robot Kadry Ali Ezzat; Lamia Nabil Omran; Ahmed Adel Ismail; Ahmed Ibrahim Bahgat El Seddawy
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp108-114

Abstract

Every country in the globe has been profoundly affected by the coronavirus epidemic, and these countries are struggling with how to clean the affected areas quickly and effectively. This project aims to contribute to the fight against the spread of the coronavirus by quickly, safely, and effectively cleaning medical clinics. Regular cleaning and disinfection might reassure people and increase their confidence in the lessened risk of the spread of communicable diseases. Robots that use ultraviolet C (UVC) sanitizers can quickly and effectively clean the clinic rooms. In addition to cleaning patient seating areas, clinical equipment, restrooms, and above controls. The use of UVC technology effectively eliminates airborne germs in medical clinics. The results of UVC disinfection performance indicated a 92% reduction in the total bacterial count (TBC) at 0.5 metres from the robot after 8 minutes of UVC irradiation.
Automatic brain tumor detection using adaptive region growing with thresholding methods Kadry Ali Ezzat; Lamia Nabil Omran; Ahmed Adel Ismail; Ahmed Ibrahim Bahgat El Seddawy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1569-1576

Abstract

Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
Co-Authors Ahmed Adel Ismail Ahmed Adel Ismail Kadry Ali Ezzat Lamia Nabil Mahdy Lamia Nabil Omran