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Kidney tumors are of different types having different characteristics and also remain challenging in the field of biomedicine. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. Accurate estimation of kidney tumor volume is essential for clinical diagnoses and therapeutic decisions related to renal diseases. The main objective of this research is to use the Computer-Aided Diagnosis (CAD) algorithms to help the early detection of kidney tumors that addresses the challenges of accurate kidney tumor volume estimation caused by extensive variations in kidney shape, size and orientation across subjects.
In this paper, have tried to implement an automated segmentati

Background: Prolactin is a hormone, as well as a cytokine which is synthesized and secreted from the anterior pituitary gland and various extra pituitary sites including immune cells under control of a superdistal promoter that contains a single nucleotide polymorphism -1149 G/T. Rheumatoid Arthritis has been associated with increased serum prolactin levels.Objectives: To investigate the association of the extra pituitary -1149 G/T promoter polymorphism among Iraqi rheumatoid arthritis patients and prolactin levels.Methods: We tested 73 patients with rheumatoid arthritis and 40 healthy individuals. The DNA samples were genotyped using the Polymerase Chain Reaction-Restriction fragment Length Polymorphism method and the levels of prolacti

       In recent years, the attention of researchers has increased of semi-parametric regression models, because it is possible to integrate the parametric and non-parametric regression models in one and then form a regression model has the potential to deal with the cruse of dimensionality in non-parametric models that occurs through the increasing of explanatory variables. Involved in the analysis and then decreasing the accuracy of the estimation. As well as the privilege of this type of model with flexibility in the application field compared to the parametric models which comply with certain conditions such as knowledge of the distribution of errors or the parametric models may

The aim of this paper is to approximate multidimensional functions f∈C(R^s) by developing a new type of Feedforward neural networks (FFNS) which we called it Greedy ridge function neural networks (GRGFNNS). Also, we introduce a modification to the greedy algorithm which is used to train the greedy ridge function neural networks. An error bound are introduced in Sobolov space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result in [1]).