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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., 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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Waleed Yamany, N. El-Bendary, Hossam 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. Advances in Intelligent Systems and Computing(Springer) , Czech republic , 2013.
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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Ella Hassanien, A., M. E. Abdelhafez, and H. S. Own, "Rough set analysis in knowledge discovery: a case of Kuwaiti diabetic children patients", Advances in Fuzzy Systems, pp. 1–13, 2007. Abstract
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Hassanien, A. E., "Rough set approach for attribute reduction and rule generation: A case of patients with suspected breast cancer", Journal of the American Society for Information Science and Technology , vol. 55, issue 11, pp. 954-962 , 2004. AbstractWebsite

Rough set theory is a relatively new intelligent technique used in the discovery of data dependencies; it evaluates the importance of attributes, discovers the patterns of data, reduces all redundant objects and attributes, and seeks the minimum subset of attributes. Moreover, it is being used for the extraction of rules from databases. In this paper, we present a rough set approach to attribute reduction and generation of classification rules from a set of medical datasets. For this purpose, we first introduce a rough set reduction technique to find all reducts of the data that contain the minimal subset of attributes associated with a class label for classification. To evaluate the validity of the rules based on the approximation quality of the attributes, we introduce a statistical test to evaluate the significance of the rules. Experimental results from applying the rough set approach to the set of data samples are given and evaluated. In addition, the rough set classification accuracy is also compared to the well-known ID3 classifier algorithm. The study showed that the theory of rough sets is a useful tool for inductive learning and a valuable aid for building expert systems.

Hassanien, A. - E., "Rough set approach for attribute reduction and rule generation: a case of patients with suspected breast cancer", Journal of the American Society for information Science and Technology, vol. 55, no. 11: Wiley Subscription Services, Inc., A Wiley Company, pp. 954–962, 2004. Abstract
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Hassanien, A. - E., "Rough set approach for attribute reduction and rule generation: a case of patients with suspected breast cancer", Journal of the American Society for information Science and Technology, vol. 55, no. 11: Wiley Subscription Services, Inc., A Wiley Company, pp. 954–962, 2004. Abstract
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Hassanien, A. E., and J. M. Ali, "Rough set approach for classification of breast cancer mammogram images", International Workshop on Fuzzy Logic and Applications: Springer Berlin Heidelberg, pp. 224–231, 2003. Abstract

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Hassanien, A. E., and J. M. Ali, "Rough set approach for classification of breast cancer mammogram images", International Workshop on Fuzzy Logic and Applications: Springer Berlin Heidelberg, pp. 224–231, 2003. Abstract
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Hassanien, A. E., and J. M. H. Ali, "Rough set approach for generation of classification rules of breast cancer data", Informatica, vol. 15, no. 1: Institute of Mathematics and Informatics, pp. 23–38, 2004. Abstract
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Hassanien, A. E., and J. M. H. Ali, "Rough set approach for generation of classification rules of breast cancer data", Informatica, vol. 15, no. 1: Institute of Mathematics and Informatics, pp. 23–38, 2004. Abstract
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Ali, J., and A. E. Hassanien, "Rough Set Approach for Generation of Classification Rules of Breast Cancer Data.", Informatica, vol. 15, issue 1, pp. 23-38, 2004. Abstract

Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing, accessing, analysis, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Recently, rough set theory is a new intelligent technique was used for the discovery of data dependencies, data reduction, approximate set classification, and rule induction from databases.

In this paper, we present a rough set method for generating classification rules from a set of observed 360 samples of the breast cancer data. The attributes are selected, normalized and then the rough set dependency rules are generated directly from the real value attribute vector. Then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Experimental results from applying the rough set analysis to the set of data samples are given and evaluated. In addition, the generated rules are also compared to the well-known IDS classifier algorithm. The study showed that the theory of rough sets seems to be a useful tool for inductive learning and a valuable aid for building expert systems.

Banerjee, S., H. Al-Qaheri, E. - S. A. El-Dahshan, and A. E. Hassanien, "Rough set approach in ultrasound biomicroscopy glaucoma analysis", Advances in Computer Science and Information Technology: Springer Berlin Heidelberg, pp. 491–498, 2010. Abstract
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Banerjee, S., H. Al-Qaheri, E. - S. A. El-Dahshan, and A. E. Hassanien, "Rough set approach in ultrasound biomicroscopy glaucoma analysis", Advances in Computer Science and Information Technology: Springer Berlin Heidelberg, pp. 491–498, 2010. Abstract
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P. K. Nizar Banu, H. H. Inbarani, A. T. Azar, H. S. Own, and A. E. Hassanien, "Rough Set Based Feature Selection for Egyptian Neonatal Jaundice ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Banu, P. K. N., H. H. Inbarani, A. T. Azar, H. S. Own, and A. E. Hassanien, "Rough set based feature selection for egyptian neonatal jaundice", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 367–378, 2014. Abstract
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Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, "Rough set generating prediction rules for stock price movement", Computer Modeling and Simulation, 2008. EMS'08. Second UKSIM European Symposium on: IEEE, pp. 111–116, 2008. Abstract
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Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, "Rough set generating prediction rules for stock price movement", Computer Modeling and Simulation, 2008. EMS'08. Second UKSIM European Symposium on: IEEE, pp. 111–116, 2008. Abstract
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Salama, M. A., O. S. Soliman, I. Maglogiannis, A. E. Hassanien, and A. A. Fahmy, "Rough set-based identification of heart valve diseases using heart sounds", Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam: Springer Berlin Heidelberg, pp. 475–491, 2013. Abstract
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Salama, M. A., O. S. Soliman, I. Maglogiannis, A. E. Hassanien, and A. A. Fahmy, "Rough set-based identification of heart valve diseases using heart sounds", Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam: Springer Berlin Heidelberg, pp. 475–491, 2013. Abstract
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Hassanien, A. E., and J. M. H. Ali, "Rough sets analysis for reduct generation of breast cancer data", INTERNATIONAL JOURNAL OF COMPUTERS AND THEIR APPLICATIONS, vol. 10: ISCA, pp. 263–270, 2003. Abstract
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Elshazly, Hanaa, N.; Ghali, A. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", World Congress on Information and Communication Technologies (WICT), pp. 260 - 265 , India, Oct. 30 2012-Nov. Abstract

The use of computational intelligence systems such as rough sets, neural networks, fuzzy set, genetic algorithms, etc., for predictions and classification has been widely established. This paper presents a generic classification model based on a rough set approach and decision rules. To increase the efficiency of the classification process, boolean reasoning discretization algorithm is used to discretize the data sets. The approach is tested by a comparatif study of three different classifiers (decision rules, naive bayes and k-nearest neighbor) over three distinct discretization techniques (equal bigning, entropy and boolean reasoning). The rough set reduction technique is applied to find all the reducts of the data which contains the minimal subset of attributes that are associated with a class label for prediction. In this paper we adopt the genetic algorithms approach to reach reducts. Finally, decision rules were used as a classifier to evaluate the performance of the predicted reducts and classes. To evaluate the performance of our approach, we present tests on breast cancer data set. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach and decision rules is high compared with other classification techniques including Bayes and k-nearest neighbor.

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