Papers by Muhammed Hammad
Scientific Reports
The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the publ... more The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural ...
2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2021
Novel Coronavirus Disease 2019 (COVID-19) is a new pandemic that appeared at the end of March 201... more Novel Coronavirus Disease 2019 (COVID-19) is a new pandemic that appeared at the end of March 2019 in Wuhan city, China, which affected millions worldwide. COVID-19 is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) epidemic. Also, several viral epidemics have been listed in the last two decades, like the middle east respiratory syndrome coronavirus (MERSCoV) and the severe acute respiratory syndrome coronavirus 1 (SARSCoV-1), which cause MERS, and SARS diseases, respectively. Detection of these viral epidemics is a difficult issue because of their genetic similarity. In this paper, an effective automated system was developed to classify these viral epidemics using their complete genomic sequences via the genomic image processing techniques to facilitate the diagnosis and increase the detection accuracy in a short time. Results achieved an overall accuracy of 100% using two classifiers: SVM and KNN. However, the KNN classifier shows a privilege over the SVM in the execution time performance.
Alexandria Engineering Journal
2016 8th Cairo International Biomedical Engineering Conference (CIBEC), 2016
Cardiovascular disease is the main cause of death worldwide. Magnetic resonance imaging (MRI) pro... more Cardiovascular disease is the main cause of death worldwide. Magnetic resonance imaging (MRI) provides unprecedented capabilities for myocardial tissue characterization. Specifically, T1 mapping showed to be a valuable technique for identifying myocardial fibrosis and scar tissues without the need for contrast agent administration. Nevertheless, various factors can influence the analysis technique and affect the resulting T1 measurements. These factors include the signal calculation method (Average, Median, or pixel-wise Mapping), size and location of the analyzed region-of-interest (ROI), and signal-to-noise ratio (SNR) level of the acquired images. In this study, we evaluate the influence of these factors on T1 measurement using numerical and calibrated phantoms. The numerical phantom results showed that the percentage error for T1 estimation reached 2.3%, 3.1%, and 12.28% for the Median, Average, and Mapping methods, respectively, at SNR level of 7 dB, compared to 0% error at high SNR (44 dB) .The calibrated phantom experiments revealed high correlation (R>0.9) between the estimated and reference T1 values at high SNR levels. Based on the results in this study, the Median calculation method outperforms the Average and Mapping methods due to its lower estimation error at low SNR and higher correlation values with reference measurements. Further, large ROI's that are placed at the center of the analyzed region result in more accurate T1 estimates than smaller ROI's or those placed closer to the object boundary.
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Papers by Muhammed Hammad