Prediction of neurodegenerative disease using brain image analysis with multilinear principal component analysis and quadratic discriminant analysis

Neuroimaging, a part of medical imaging is playing a crucial role in the field of neuroscience and psychology for analysing the structure as well as functions of the internal nervous system. It is the most evolving and prominent method in medical science as it not only focuses on the structure, but also the functional aspects of the brain. It helps in diagnosing metabolic and neurodegenerative diseases. More precisely, magnetic resonance imaging is one of the cutting-edge technologies in the medical verdict for the past four decades in diagnosing Neuro disorders. In recent years, machine learning and other high-end reckoning tools are used universally in assisting the automated identification of neurodegenerative diseases such as Parkinson's disease (PD), Alzheimer's disease (AD) and so on. However, similarities exist in the functional part of the nervous system, making it difficult for the learning algorithms to classify the disease. The main goal of this article is to predict neurodegenerative diseases in an effective way using functional analysis of brain images. The proposed model for predicting neurodegenerative diseases based on brain image analysis has two main phases. The first phase processes the image by applying pre-processing techniques and extracts the features from the images using wavelet transform and histogram gradients for further processing. The second phase applies machine learning algorithms such as multilinear principal component analysis and regularized discriminant analysis for selecting the features and identifying the prediction labels respectively. The experimental analysis has been performed with various brain image datasets such as DS-66, DS-75, DS-160 and DS-255. The obtained results show the 100% accuracy on DS-66, DS-75, and DS-160 datasets and 99.58% accuracy on DS-255 and take a minimum computational overhead of 0.025s. Thus, the proposed model offers improved results than many other existing models. Also, the performance analysis using various quality metrics endorses the significance of the proposed model in diagnosing neurodegenerative disease.