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Ahmad. Taher Azar, A. E. Hassanien, and T. - H. Kim, "Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis.", International Conference on Bio-Science and Bio-Technology (BSBT2012), , Kangwondo, Korea. pp. 94--105, December 16-19, 2012. Abstract3530094.pdf

The thyroid, an endocrine gland that secretes hormones in the blood, circulates its products to all tissues of the body, where they control vital functions in every cell. Normal levels of thyroid hormone help the brain, heart, intestines, muscles and reproductive system function normally. Thyroid hormones control the metabolism of the body. Abnormalities of thyroid function are usually related to production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism). Therefore, the correct diagnosis of these diseases is very important topic. In this study, Linguistic Hedges Neural-Fuzzy Classifier with Selected Features (LHNFCSF) is presented for diagnosis of thyroid diseases. The performance evaluation of this system is estimated by using classification accuracy and k-fold cross-validation. The results indicated that the classification accuracy without feature selection was 98.6047% and 97.6744% during training and testing phases, respectively with RMSE of 0.02335. After applying feature selection algorithm, LHNFCSF achieved 100% for all cluster sizes during training phase. However, in the testing phase LHNFCSF achieved 88.3721% using one cluster for each class, 90.6977% using two clusters, 91.8605% using three clusters and 97.6744% using four clusters for each class and 12 fuzzy rules. The obtained classification accuracy was very promising with regard to the other classification applications in literature for this problem.

Abdo, W., Evolutionary Computation in Cryptanalysis, , Cairo Egypt, Al Azhar University and Scientific Research Group in Egypt (SRGE), 2013. ppt_phd_thesis_on_EC_CA.pdfphd_thesis_EC_CA_2013.pdf
Abder-Rahman Ali, Micael Couceiro, A. M. Anter, and A. E. Hassanien, "Evaluating an Evolutionary Particle Swarm Optimization for Fast Fuzzy C-Means Clustering on Liver CT Images", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, USA, IGI, 2014. Abstract

An Evolutionary Particle Swarm Optimization based on the Fractional Order Darwinian method for
optimizing a Fast Fuzzy C-Means algorithm is proposed. This chapter aims at enhancing the performance
of Fast Fuzzy C-Means, both in terms of the overall solution and speed. To that end, the concept
of fractional calculus is used to control the convergence rate of particles, wherein each one of them
represents a set of cluster centers. The proposed solution, denoted as FODPSO-FFCM, is applied on
liver CT images, and compared with Fast Fuzzy C-Means and PSOFFCM, using Jaccard Index and
Dice Coefficient. The computational efficiency is achieved by using the histogram of the image intensities
during the clustering process instead of the raw image data. The experimental results based on the
Analysis of Variance (ANOVA) technique and multiple pair-wise comparison show that the proposed
algorithm is fast, accurate, and less time consuming.