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Magnetic Resonance Images (MRI) of the Brain is a significant tool to diagnosis Alzheimer's disease due to its ability to measure regional changes in the brain that reflect disease progression to detect early stages of the disease. In this paper, we propose a new model that adopts Bat for parameter optimization problem of Support vector machine (SVM) to diagnose Alzheimer’s disease via MRI biomedical image. The proposed model uses MRI for biomedical image classification to diagnose three classes; normal controls (NC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The proposed model based on segmentation for the most involved areas in the disease hippocampus, the features of MRI brain images are extracted to build feature vector of the brain, then extracting the most significant features in neuroimaging to reduce the high dimensional space of MRI images to lower dimensional subspace, and submitted to machine learning classification technique. Moreover, the model is applied on different datasets to validate the efficiency which show that the new Bat-SVM model can yield promising acceptable level of accuracy reached to 95.36 % using maximum number of bats equal to 50 and number of generation equal to 10.

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Image segmentation is a basic technique for advanced image analysis. In this paper a new image segmentation algorithm based on combining particle swarm optimization PSO and rough set is proposed. The algorithm adopts mean roughness measure as evaluation standard, this measure depends on minimization of roughness in both object and background regions; by determining the optimal threshold of partitioning. In this algorithm, threshold estimation is regarded as a search procedure that searches for an optimal value in a continuous gray-scale interval. The results of the PSO based proposed algorithm are compared with Bat-Inspired algorithm under mean roughness measure as the fitness function and simulations show that the PSO seems much superior than Bat algorithm.

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