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Sayed, G. I., A. E. Hassanien, and G. Schaefer, "An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images", Procedia Computer Science, vol. 90: Elsevier, pp. 68–73, 2016. Abstract
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Sayed, G. I., M. A. Ali, T. Gaber, A. E. Hassanien, and V. Snasel, "A hybrid segmentation approach based on neutrosophic sets and modified watershed: a case of abdominal CT Liver parenchyma", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 144–149, 2015. Abstract
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Sayed, G. I., and A. E. Hassanien, "Neuro-Imaging Machine Learning Techniques for Alzheimer's Disease Diagnosis", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 522–540, 2017. Abstract
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Sayed, G. I., and A. E. Hassanien, "A hybrid SA-MFO algorithm for function optimization and engineering design problems", Complex & Intelligent Systems, 2018. Abstract

This paper presents a hybrid algorithm based on using moth-flame optimization (MFO) algorithm with simulated annealing (SA), namely (SA-MFO). The proposed SA-MFO algorithm takes the advantages of both algorithms. It takes the ability to escape from local optima mechanism of SA and fast searching and learning mechanism for guiding the generation of candidate solutions of MFO. The proposed SA-MFO algorithm is applied on 23 unconstrained benchmark functions and four well-known constrained engineering problems. The experimental results show the superiority of the proposed algorithm. Moreover, the performance of SA-MFO is compared with well-known and recent meta-heuristic algorithms. The results show competitive results of SA-MFO concerning MFO and other meta-heuristic algorithms.

Sayed, G. I., and A. E. Hassanien, "Abdominal CT Liver Parenchyma Segmentation Based on Particle Swarm Optimization", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 219–228, 2016. Abstract
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Sayed, G. I., A. E. Hassanien, M. A. Ali, and T. Gaber, "A Hybrid segmentation approach based on Neutrosophic sets and modified watershed: A case of abdominal CT liver parenchyma", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Sayed, G. I., A. Darwish, A. E. Hassanien, and J. - S. Pan, "Breast Cancer Diagnosis Approach Based on Meta-Heuristic Optimization Algorithm Inspired by the Bubble-Net Hunting Strategy of Whales", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 306–313, 2016. Abstract
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Sayed, G. I., and A. E. Hassanien, "Neuro-Imaging Machine Learning Techniques for Alzheimer's Disease Diagnosis ", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Alzheimer's disease (AD) is considered one of the most common dementia's forms affecting senior's age staring from 65 and over. The standard method for identifying AD are usually based on behavioral, neuropsychological and cognitive tests and sometimes followed by a brain scan. Advanced medical imagining modalities such as MRI and pattern recognition techniques are became good tools for predicting AD. In this chapter, an automatic AD diagnosis system from MRI images based on using machine learning tools is proposed. A bench mark dataset is used to evaluate the performance of the proposed system. The adopted dataset consists of 20 patients for each diagnosis case including cognitive impairment, Alzheimer's disease and normal. Several evaluation measurements are used to evaluate the robustness of the proposed diagnosis system. The experimental results reveal the good performance of the proposed system.

Sayed, G. I., A. E. Hassanien, and G. Schaefer, "An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images ", Procedia Computer Science, , vol. 90 , pp. Pages 68-73, 2016. AbstractWebsite

In this paper, we present a computer-aided diagnosis (CAD) system for abdominal Computed Tomography liver images that comprises four main phases: liver segmentation, lesion candidate segmentation, feature extraction from each candidate lesion, and liver disease classification. A hybrid approach based on fuzzy clustering and grey wolf optimisation is employed for automatic liver segmentation. Fast fuzzy c-means clustering is used for lesion candidates extraction, and a variety of features are extracted from each candidate. Finally, these features are used in a classification stage using a support vector machine. Experimental results confirm the efficacy of the proposed CAD system, which is shown to yield an overall accuracy of almost 96% in terms of healthy liver extraction and 97% for liver disease classification.

Sayed, G. I., M. Soliman, and A. E. Hassanien, "Modified Optimal Foraging Algorithm for Parameters Optimization of Support Vector Machine", International Conference on Advanced Machine Learning Technologies and Applications, Cairo, 23 Feb, 2018. Abstract

Support Vector Machine (SVM) is one of the widely used algorithms for classification and regression problems. In SVM, penalty parameter C and kernel parameters can have a significant impact on the complexity and performance of SVM. In this paper, an Optimal Foraging Algorithm (OFA) is proposed to optimize the main parameters of SVM and reduce the classification error. Six public benchmark datasets were employed for evaluating the proposed (OFA-SVM). Also, five well-known and recently optimization algorithms are used for evaluation. These algorithms are Artificial Bee Colony (ABC), Genetic Algorithm (GA), Chicken Swarm Optimization (CSO), Particle Swarm Optimization (PSO) and Bat Algorithm (BA). The experimental results show that the proposed OFA-SVM obtained superior results. Also, the results demonstrate the capability of the proposed OFA-SVM to find optimal values of SVM parameters.

Sayed, G. I., and A. E. Hassanien, "Interphase cells removal from metaphase chromosome images based on meta-heuristic grey wolf optimizer", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Sayed, G. I., M. Soliman, and A. E. Hassanien, "Bio-inspired Swarm Techniques for Thermogram Breast Cancer Detection", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 487–506, 2016. Abstract
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Sayed, G. I., and A. E. Hassanien, "Interphase cells removal from metaphase chromosome images based on meta-heuristic Grey Wolf Optimizer", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 261–266, 2015. Abstract
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Sayed, G. I., and A. E. Hassanien, "Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images", Applied Intelligence: Springer US, pp. 1–12, 2017. Abstract
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Schaefer, G., H. Zhou, Qinghua Hu, and A. E. Hassanien, "Rough image colour quantisation", International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing: Springer Berlin Heidelberg, pp. 217–222, 2009. Abstract
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Schaefer, G., Bartosz Krawczyk, E. M. Celebi, H. Iyatomi, and A. E. Hassanien, "Melanoma Classification based on Ensemble Classification of Dermoscopy Image Features", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Schaefer, G., Qinghua Hu, H. Zhou, J. F. Peters, and A. E. Hassanien, "Rough c-means and fuzzy rough c-means for colour quantisation", Fundamenta Informaticae, vol. 119, no. 1: IOS Press, pp. 113–120, 2012. Abstract
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Schaefer, G., Qinghua Hu, H. Zhou, J. F. Peters, and A. E. Hassanien, "Rough c-means and fuzzy rough c-means for colour quantisation", Fundamenta Informaticae, vol. 119, no. 1: IOS Press, pp. 113–120, 2012. Abstract
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Schaefer, G., Niraj P. Doshi, Qinghua Hu, and A. E. Hassanien, "Classification of HEp-2 Cell Images using Compact Multi-Scale Texture Information and Margin Distribution Based Bagging ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Schaefer, G., H. Zhou, Qinghua Hu, and A. E. Hassanien, "Rough image colour quantisation", International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing: Springer Berlin Heidelberg, pp. 217–222, 2009. Abstract
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Schaefer, G., N. P. Doshi, Qinghua Hu, and A. E. Hassanien, "Classification of HEp-2 Cell Images Using Compact Multi-Scale Texture Information and Margin Distribution Based Bagging", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 299–308, 2014. Abstract
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Schaefer, G., A. Hassanien, and J. Jiang, Computational Intelligence in Medical Imaging: Techniques and Applications, : CRC press, 2009. Abstract
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Schaefer, G., Bartosz Krawczyk, E. M. Celebi, H. Iyatomi, and A. E. Hassanien, "Melanoma classification based on ensemble classification of dermoscopy image features", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 291–298, 2014. Abstract
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Schaefer, G., H. Zhou, E. M. Celebi, and A. E. Hassanien, "Rough colour quantisation", International Journal of Hybrid Intelligent Systems, vol. 8, no. 1: IOS Press, pp. 25–30, 2011. Abstract
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Schaefer, G., H. Zhou, E. M. Celebi, and A. E. Hassanien, "Rough colour quantisation", International Journal of Hybrid Intelligent Systems, vol. 8, no. 1: IOS Press, pp. 25–30, 2011. Abstract
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