Examinando por Autor "Mahmoudi, Ramzi"

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    A deep learning-based diagnosis system for COVID-19 detection and pneumonia screening using CT imaging
    (MDPI, 2022-05-10) Mahmoudi, Ramzi; Benameur, Narjes ; Mabrouk, Rania; Mohammed, Mazin Abed; García-Zapirain, Begoña; Bedoui, Mohamed Hédi
    Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a global threat impacting the lives of millions of people worldwide. Automated detection of lung infections from Computed Tomography scans represents an excellent alternative; however, segmenting infected regions from CT slices encounters many challenges. Developing a diagnosis system based on deep learning techniques to detect and quantify COVID-19 infection and pneumonia screening using CT imaging. Contrast Limited Adaptive Histogram Equalization pre-processing method was used to remove the noise and intensity in homogeneity. Black slices were also removed to crop only the region of interest containing the lungs. A U-net architecture, based on CNN encoder and CNN decoder approaches, is then introduced for a fast and precise image segmentation to obtain the lung and infection segmentation models. For better estimation of skill on unseen data, a fourfold cross-validation as a resampling procedure has been used. A three-layered CNN architecture, with additional fully connected layers followed by a Softmax layer, was used for classification. Lung and infection volumes have been reconstructed to allow volume ratio computing and obtain infection rate. Starting with the 20 CT scan cases, data has been divided into 70% for the training dataset and 30% for the validation dataset. Experimental results demonstrated that the proposed system achieves a dice score of 0.98 and 0.91 for the lung and infection segmentation tasks, respectively, and an accuracy of 0.98 for the classification task. The proposed workflow aimed at obtaining good performances for the different system’s components, and at the same time, dealing with reduced datasets used for training.
  • Miniatura
    Ítem
    Parametric methods for the regional assessment of cardiac wall motion abnormalities: comparison study
    (Tech Science Press, 2021-06-04) Benameur, Narjes; Mohammed, Mazin Abed ; Mahmoudi, Ramzi ; Arous, Younes; García-Zapirain, Begoña ; Abdulkareem, Karrar Hameed; Bedoui, Mohamed Hédi
    Left ventricular (LV) dysfunction is mainly assessed by global contractile indices such as ejection fraction and LV Volumes in cardiac MRI. While these indices give information about the presence or not of LV alteration, they are not able to identify the location and the size of such alteration. The aim of this study is to compare the performance of three parametric imaging techniques used in cardiac MRI for the regional quantification of cardiac dysfunction. The proposed approaches were evaluated on 20 patients with myocardial infarction and 20 subjects with normal function. Three parametric images approaches: covariance analysis, parametric images based on Hilbert transform and those based on the monogenic signal were evaluated using cine-MRI frames acquired in three planes of views. The results show that parametric images generated from the monogenic signal were superior in term of sensitivity (89.69%), specificity (86.51%) and accuracy (89.06%) to those based on covariance analysis and Hilbert transform in the detection of contractile dysfunction related to myocardial infarction. Therefore, the parametric image based on the monogenic signal is likely to provide additional regional indices about LV dysfunction and it may be used in clinical practice as a tool for the analysis of the myocardial alterations.