Alzheimer Classifications Combining Machine Learning and Signal Processing

Abstract

Manual detection of presence of Alzheimer even determination of its intensity from Magnetic Resonance Imaging (MRI) image is an easy task when a patient is heavily affected by the disease. The situation becomes cumbersome for a physician when a very mild affected image comes under consideration. For cases involving subtle differences in images, Machine Learning (ML) and Deep Learning (DL) classification are considered the best solutions. In this paper, Alzheimer-affected images of four categories —mild impairment, moderate impairment, very mild impairment, and no impairment — are taken from a benchmark open database. The image set is converted to a numerical feature vector using Speeded-Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Harris–Stephen’s, and minimum eigenvalue. The feature vector is then applied to several ML algorithms: Fuzzy Inference System (FIS), Multiple Linear Regression (MLR), Fuzzy C-Means (FCM), Naïve Bayes and linear Support Vector Machine (SVM) to check their eligibility in classifying these four types of medical images. The accuracy of detection from any individual method ranges from 56% to 68%. However, applying the maximum voting scheme to all the MLs yields an accuracy of 75%. The long feature vector is also extracted from the discrete wavelet transform (DWT), and classification is done from its coherence, but the accuracy is found to be very poor. Next, the image dataset is applied to the Bag of Features (BoF) algorithm using 500 visual words, yielding a moderate result with an accuracy of 84.28%. Finally, two deep learning models are applied: PyTorch Convolutional Neural Network (CNN) and Keras Visual Geometry Group (VGG) 19. These models are tested on 20% of the trained images, achieving accuracies of 95.7% and 97.1%, respectively.