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Waleed Yamany, N. El-Bendary, H. M. Zawbaa, A. E. Hassanien, and Václav Snášel, "Rough Power Set Tree for Feature Selection and Classification: Case Study on MRI Brain Tumor", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 259–270, 2014. Abstract
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Hassanien, A. E., and D. Slezak, "Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer", International Journal of Hybrid Intelligent Systems, vol. 3, issue 4, pp. 205-218 , 2006. AbstractWebsite

The objective of this paper is to introduce a rough neural intelligent approach for rule generation and image classification. Hybridization of intelligent computing techniques has been applied to see their ability and accuracy to classify breast cancer images into two outcomes: malignant cancer or benign cancer. Algorithms based on fuzzy image processing are first applied to enhance the contrast of the whole original image; to extract the region of interest and to enhance the edges surrounding that region. Then, we extract features characterizing the underlying texture of the regions of interest by using the gray-level co-occurrence matrix. Then, the rough set approach to attribute reduction and rule generation is presented. Finally, rough neural network is designed for discrimination of different regions of interest to test whether they represent malignant cancer or benign cancer. Rough neural network is built from rough neurons, each of which can be viewed as a pair of sub-neurons, corresponding to the lower and upper bounds. To evaluate performance of the presented rough neural approach, we run tests over different mammogram images. The experimental results show that the overall classification accuracy offered by rough neural approach is high compared with other intelligent techniques

Ślęzak, D., and others, "Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer", International Journal of Hybrid Intelligent Systems, vol. 3, no. 4: IOS Press, pp. 205–218, 2006. Abstract
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Ślęzak, D., and others, "Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer", International Journal of Hybrid Intelligent Systems, vol. 3, no. 4: IOS Press, pp. 205–218, 2006. Abstract
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Hassanien, A. E., and A. Abraham, "Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction", International Journal of Computational Intelligence and Applications , vol. 7, issue 1, pp. 17-42 , 2008. AbstractWebsite

The objective of this research is to illustrate how rough sets can be successfully integrated with mathematical morphology and provide a more effective hybrid approach to resolve medical imaging problems. Hybridization of rough sets and mathematical morphology techniques has been applied to depict their ability to improve the classification of breast cancer images into two outcomes: malignant and benign cancer. Algorithms based on mathematical morphology are first applied to enhance the contrast of the whole original image; to extract the region of interest (ROI) and to enhance the edges surrounding that region. Then, features are extracted characterizing the underlying texture of the ROI by using the gray-level co-occurrence matrix. The rough set approach to attribute reduction and rule generation is further presented. Finally, rough morphology is designed for discrimination of different ROI to test whether they represent malignant cancer or benign cancer. To evaluate performance of the presented rough morphology approach, we tested different mammogram images. The experimental results illustrate that the overall performance in locating optimal orientation offered by the proposed approach is high compared with other hybrid systems such as rough-neural and rough-fuzzy systems.

Hassanien, A. E., and A. Abraham, "Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction", International Journal of Computational Intelligence and Applications, vol. 7, no. 01: Imperial College Press, pp. 17–42, 2008. Abstract
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Hassanien, A. E., and A. Abraham, "Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction", International Journal of Computational Intelligence and Applications, vol. 7, no. 01: Imperial College Press, pp. 17–42, 2008. Abstract
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Hassanien, A. E., Mostafa A. Salama, J. Platos, and V. Snásel, "Rough local transfer function for cardiac disorders detection using heart sounds. ", Logic Journal of the IGPL, vol. 23, issue 3, pp. 506-520, 2015. Website
Hassanien, A. E., M. Salama, J. Platos, and V. Snasel, "Rough local transfer function for cardiac disorders detection using heart sounds ", Logic Journal of the IGPL, Oxford, vol. (in press), 2014. Website
Hassanien, A. E., M. A. Salama, J. Platos, and V. Snasel, "Rough local transfer function for cardiac disorders detection using heart sounds", Logic Journal of IGPL: Oxford University Press, pp. jzv009, 2015. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and T. - H. Kim, "A rough k-means fragile watermarking approach for image authentication", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 19–23, 2011. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and T. - H. Kim, "A rough k-means fragile watermarking approach for image authentication", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 19–23, 2011. 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., 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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Hassanien, A. E., H. Al-Qaheri, and A. Abraham, "Rough Hybrid Scheme", Rough Fuzzy Image Analysis: Foundations and Methodologies: CRC Press, pp. 5–1, 2010. Abstract
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Hassanien, A. E., Rough Computing: Theories, Technologies, and Applications, , USA, IGI Global USA, 2008. AbstractWebsite

Rough set theory is a new soft computing tool which deals with vagueness and uncertainty. It has attracted the attention of researchers and practitioners worldwide, and has been successfully applied to many fields such as knowledge discovery, decision support, pattern recognition, and machine learning. Rough Computing: Theories, Technologies and Applications offers the most comprehensive coverage of key rough computing research, surveying a full range of topics from granular computing to pansystems theory. With its unique coverage of the defining issues of the field, this commanding research collection provides libraries with a single, authoritative reference to this highly advanced technological topic.

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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Soliman, O. S., A. E. Hassanien, and N. El-Bendary, "A rough clustering algorithm based on entropy information", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 213–222, 2011. Abstract
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Soliman, O. S., A. E. Hassanien, and N. El-Bendary, "A rough clustering algorithm based on entropy information", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 213–222, 2011. 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., 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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