Research Area: Machine Learning
The most prevalent and common type of dementia is Alzheimers 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 radiologists 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 Alzheimers 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 Alzheimers Disease Neuroimaging Institute (ADNI).
Keywords:
Alzheimers disease
Machine learning
Content-based image retrieval
Multistage classifier
PSO
Structural MRI
SVM
K-NN
Author(s) Name: K.R. Kruthika, Rajeswari, H.D. Maheshappa
Journal name: Informatics in Medicine Unlocked
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.imu.2018.12.003
Volume Information: Volume 14
