Nonlinear Feature Extraction Methods Based on Dual-Tree Complex Wavelet Transform Subimages of Brain Magnetic Resonance Imaging for the Classification of Multiple Diseases.

It has been a long time since we use magnetic resonance imaging (MRI) to detect brain diseases and many useful techniques have been developed for this task. However, there is still a potential for further improvement of classification of brain diseases in order to be sure of the results. In this research we presented, for the first time, a non-linear feature extraction method from the MRI sub-images that are obtained from the three levels of the two-dimensional Dual tree complex wavelet transform (2D DT-CWT) in order to classify multiple brain disease. After extracting the non-linear features from the sub-images, we used the spectral regression discriminant analysis (SRDA) algorithm to reduce the classifying features. Instead of using the deep neural networks that are computationally expensive, we proposed the Hybrid RBF network that uses the k-means and recursive least squares (RLS) algorithm simultaneously in its structure for classification. To evaluate the performance of RBF networks with hybrid learning algorithms, we classify nine brain diseases based on MRI processing using these networks, and compare the results with the previously presented classifiers including, supporting vector machines (SVM) and K-nearest neighbour (KNN). Comprehensive comparisons are made with the recently proposed cases by extracting various types and numbers of features. Our aim in this paper is to reduce the complexity and improve the classifying results with the hybrid RBF classifier and the results showed 100 percent classification accuracy in both the two class and the multiple classification of brain diseases in 8 and 10 classes. In this paper, we provided a low computational and precise method for brain MRI disease classification. the results show that the proposed method is not only accurate but also computationally reasonable.

Astrodaucus persicus as a new source of bioinsectisides against malaria vector, Anopheles stephensi.

OBJECTIVE: To determine the larvicidal activities of petroleum ether, chloroform, ethyl acetate and methanol fractions of roots and fruits extracts of Astrodaucus persicus from Apiaceae family against malaria vector, Anopheles stephensi (An. stephensi). METHODS: Twenty five third instar larvae of An. stephensi were exposed to various concentrations (10-160 g/L) of fractions and were assayed according to World Health Organization protocol. The larval mortality was calculated after 24 h treatment. RESULTS: Among tested fractions, the highest larvicidal efficacy was observed from ethyl acetate fraction of fruits extract with 50% and 90% mortality values (LC50 and LC90) of 34.49 g/L and 108.61 g/L, respectively. Chloroform fraction of fruits extract was the second larvicidal sample with LC50 of 45.11 g/L and LC90 of 139.36 g/L. Petroleum ether fractions of fruits and roots and methanol fraction of fruits showed moderate toxicity against An. stephensi. CONCLUSIONS: Astrodaucus persicus is a potential source of valuable and natural larvicidal compounds against malaria vector, An. stephensi and can be used in mosquitoes control programs as an alternative to synthetic insecticides.