Publications

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A
Abd-Elmoniem, K. Z., I. A. Yassine, N. S. Metwalli, A. Hamimi, R. Ouwerkerk, J. R. Matta, M. Wessel, M. A. Solomon, J. M. Elinoff, A. M. Ghanem, et al., "Direct pixel to pixel principal strain mapping from tagging MRI using end to end deep convolutional neural network (DeepStrain)", Scientific Reports, vol. 11, issue 1, pp. 23021, 2021.
Abdeldayem, S., and I. A. Yassine, "Multi-Scale Hybrid Filter For Vasculature Extraction Enhancement", IEEE- Engineering in Medicine and Biology Conference, Milan, Italy, 2015.
Abduallatif, N. A., Sara G Elsherbini, B. S. Boshra, and I. A. A. Yassine, "Brain-Computer Interface controlled functional electrical stimulation system for paralyzed arm", 8th Cairo International Biomedical Engineering Conference (CIBEC), , Cairo, Egypt, pp. 48-51, 2016.
Abd‐Elmoniem, K. Z., I. A. Yassine, N. S. Metwalli, A. Hamimi, R. Ouwerkerk, J. R. Matta, M. Wessel, M. A. Solomon, J. M. Elinoff, A. M. Ghanem, et al., "Direct pixel to pixel principal strain mapping from tagging MRI using end to end deep convolutional neural network (DeepStrain)", Scientific Reports, vol. 11, pp. 20321, 2021.
Abrahim, B. A., Z. A. Mustafa, I. A. Yassine, N. Zayed, and Y. M. Kadah, "Hybrid total variation and wavelet thresholding Speckle reduction for medical Ultrasound", Journal of Medical Imaging and Health Informatics, vol. 2, issue 2, pp. 114-124, 2012.
Algumaei, A. H., R. F. Algunaid, M. A. Rushdi, and I. A. Yassine, "Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data", PlosOne, vol. 17, issue 5, pp. e0265300, 2022.
Algumaei, A. H., R. F. Algunaid, M. A. Rushdi, and I. A. Yassine, "Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data.", PloS one, vol. 17, issue 5, pp. e0265300, 2022. Abstract

Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of 70 schizophrenic subjects and 70 healthy subjects. The decision-level fusion method outperformed the single-feature-type approaches and achieved a 97.85% accuracy, a 98.33% sensitivity, a 96.83% specificity. Moreover, feature-fusion scheme resulted in a 98.57% accuracy, a 99.71% sensitivity, a 97.66% specificity, and an area under the ROC curve of 0.9984. In general, decision-level and feature-level fusion schemes boosted the performance of schizophrenia detectors based on fMRI features.

Algunaid, R. F., A. H. Algumaei, M. A. Rushdi, and I. A. Yassine, "Schizophrenic patient identification using graph-theoretic features of resting-state fMRI data", Biomedical Signal Processing and Control , vol. 43, pp. 289–299, 2018.
Alkhiary, Y. M., T. M. Nassef, I. A. Yassine, S. B.Tayel, and A. E. S. Ezzat, "A New Computational Model to Analyze Stress Distribution of TMJ Disc from 2-D MRI Scans", Advances in Computing, vol. 2, issue 5, pp. 66-75, 2012.
B
Bibars, M., P. E. Salah, A. Eldeib, M. A. Elattar, and I. A. Yassine, "Cross-Modality Deep Transfer Learning: Application to Liver Segmentation in CT and MRI", Annual Conference on Medical Image Understanding and Analysis, UK, pp. 96-110, June, 2023.
E
Eldeeb, G., N. Zayed, and I. A. Yassine, "Alzheimer’s Disease Classification Using Bag-of-Words Based on Visual Pattern of Diffusion Anisotropy for DTI Imaging", 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, Hawaii, pp. 57-60, 2018.
Elmahdy, M. S., S. S. Abdeldayem, and I. A. Yassine, "Low quality dermal image classification using transfer learning", IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) , pp. 373-376, 2017.
Emad, O., I. A. Yassine, and A. S. Fahmy, "Automatic Localization of the Left Ventricle in Cardiac MRI Images Using Deep Learning", IEEE- Engineering in Medicine and Biology Conference, Milan, Italy, pp. 683-686, 2015.
Esmail, E. H., Hadeel M. Seif El Dein, I. A. Yassine, and R. Zakaria, "Thalamocortical Tracts, but not the Putamen, Present Microstructural Abnormalities in Juvenile Myoclonic Epilepsy: A Diffusion Tractography Study", Journal of Pediatric Epilepsy, 2018.
F
Fawzy, A., and I. A. Yassine, "Spectral Correlation Analysis For Microcalcification Detection In Digital Mammogram Images", International Symposium in Biomedical Imaging ISBI, Brooklyn, NY, USA, April, 16, 2015.
Fawzy, A., M. I. Owis, and I. A. Yassine, "Novel Bayesian Classifier discriminant function Optimization Strategies for Arythmia Classification", Biomedical and health Informatics (BHI), Valencia, Spain, 693-696, 2014.
K
Kadah, Y. M., X. Ma, S. LaConte, I. Yassine, and X. Hu, "Robust multicomponent modeling of diffusion tensor magnetic resonance imaging data", Medical Imaging: International Society for Optics and Photonics., pp. 148-159, 2005.
Kadah, Y. M., and I. A. Yassine, "Multi-Component Fiber Track Modeling of Diffusion Weighted Magnetic Resonance Imaging Data", Journal of Advanced Research , vol. 1, issue 1, pp. 26-38, 2009.
Kalaf, A. F., and I. A. Yassine, "Novel Features For Microcalci-fication Detection In Digital Mammogram Images based On Wavelet And Statistical Analysis", IEEE-International Conference on Image Processing, Quebec, Canada, 2015.
Khalaf, A. F., I. A. Yassine, and A. S. Fahmy, "Convolutional neural networks for deep feature learning in retinal vessel segmentation ", IEEE International Conference on Image Processing (ICIP), Phoenix, USA, Sept, 2016.
Khalaf, A. F., M. I. Owis, and I. A. Yassine, "Image Features of Spectral Correlation Function for Arrhythmia Classification", IEEE- Engineering in Medicine and Biology Conference, Milan, Italy, pp. 5199 - 5202, 2015.
Khalalf, A. F., M. I. Owis, and I. A. Yassine, "A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines", Expert Systems with Applications, vol. 42, pp. 8361–8368, 2015.
Khder, S. M., E. A. H. Mohamed, and I. A. Yassine, "A Clustering- based Fusion System for Blastomere Localization", Biomedical Engineering: Applications, Basis and Communications, vol. 34, issue 4, pp. 2250021, 2022.
Khedr, S. M., E. A. H. Mohamed, and I. A. Yassine, "A Clustering-based Fusion System for Blastomere Localization", Biomedical Engineering: Applications, Basis and Communications, pp. 2250021, 2022.
Koko, R. R. Z., I. A. Yassine, M. A. Wahed, J. K. Madete, and M. A. Rushdi, "Dynamic Construction of Outlier Detector Ensembles With Bisecting K-Means Clustering", EEE Access, vol. 11, pp. 24431-24447, 2023.
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