The work proposes a computer-based diagnosis method (CBDM) to delineate and assess the corpus callosum (CC) segment from the 2-dimensional (2D) brain magnetic resonance images (MRI). The proposed CBDM consists of two parts: (1) preprocessing and (2) postprocessing sections. The preprocessing tools have a multithreshold technique with the chaotic cuckoo search (CCS) algorithm and a preferred threshold procedure. The postprocessing employs a delineation process for extracting the CC section. The proposed CBDM finally extracts the vital CC parameters, such as total brain area (TBA) and CC area (CCA) to classify the considered 2D MRI slices into the control and autism spectrum disorder (ASD) groups. This attempt considers the benchmark brain MRI database which includes ABIDE and MIDAS for the experimental investigation. The results obtained with ABIDE dataset are further confirmed against the fuzzy
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-means driven level set (FCM + LS) and multiphase level set (MLS) technique and the proposed CBDM with Shannon entropy along with active contour (SE + AC) presented improved result in comparison to the existing methodologies. Further, the performance of CBDM is confirmed on MIDAS and clinical dataset. The experimental outcomes approve that the proposed CBDM extracts the CC section from the 2D MR brain images that have higher accuracy compared to alternative techniques.
Data Mining is the efficient knowledge discovery form database. It is also form of knowledge discovery essential for solving problem in specific domain like health care, business and other field. The proposed system is based on population based on heuristic search technique, which can used to solve combinatorial optimization problem. Our research focus on studying the hybrid algorithm that result in performance and enhancement in classification rule discovery task. In standard Particle Swarm Optimization (PSO) the non oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on suboptimal solution that is not even guaranteed to local optimal solution. In this paper we have present novel hybrid algorithm, PSO with Dynamic Inertia Weight and Genetic Algorithm (GA) approach for classification rule. The selection of inertia weight was very important to ensure the convergent behavior of particle In this hybrid algorithm approach incorporates a dynamic inertia weight in order to help the algorithm to find global and overcome the problem convergence to local optima, essentially GA can perform a global search over the entire search space with faster convergence speed. Thus the hybrid algorithm is easily implemented because of use of simple classifier it has, its computational complexity is low, are the special characteristics for the use of this hybrid algorithm.
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