Multistage classifier-based approach for Alzheimer's disease prediction and retrieval

The most prevalent and common type of dementia is Alzheimer's disease (AD). However, it is notable that very few people who are suffering from AD are diagnosed correctly and in a timely manner. The definite cause and cure of the disease are still unavailable. The symptoms might be more manageable and its treatment can be more effective, when the impairment is still at an earlier stage or at MCI (mild cognitive impairment). AD can be clinically diagnosed by physical and neurological examination, so there is an need for developing better and efficient diagnostic tools for AD. In recent years, content-based image retrieval (CBIR) systems have been widely researched and applied in many medical applications. Combining an automated image classification system and the radiologist's professional knowledge, to increase the accuracy of prediction and diagnosis, were the main motives. In this paper, a multistage classifier using machine learning, including Naive Bayes classifier, support vector machine (SVM), and K-nearest neighbor (KNN), was used to classify Alzheimer's disease more acceptably and efficiently. For this, MRI (Magnetic resonance imaging) scans were processed by FreeSurfer, a powerful software tool suitable for processing and normalizing brain MRI images. We also applied a feature selection technique - PSO (particle swarm optimization) to many feature vectors in order to obtain the best features that represent the salient characteristics of AD. The results of the proposed method outperform individual techniques in a benchmark database provided by the Alzheimer's Disease Neuroimaging Institute (ADNI). Keywords: Alzheimer's disease, Machine learning, Content-based image retrieval, Multistage classifier, PSO, Structural MRI, SVM, K-N

Cluster pre-existence probability

Pre-existence probability of the fragments for the complete binary spectrum of different systems such as 56Ni, 116Ba, 226Ra and 256Fm are calculated, from the overlapping part of the interaction potential using the WKB approximation. The role of reduced mass as well as the classical hydrodynamical mass in the WKB method is analysed. Within WKB, even for negative Q -value systems, the pre-existence probability is calculated. The calculations reveal rich structural information. The calculated results are compared with the values of preformed cluster model of Gupta and collaborators. The mass asymmetry motion is shown here for the first time as a part of relative separation motion