Machine Learning for Diagnosis of Alzheimer’s Disease and Early Stages

Julio José Prado,Ignacio Rojas

doi:10.3390/biomedinformatics1030012

Abstract

According to the WHO, approximately 50 million people worldwide have dementia and there are nearly 10 million new cases every year. Alzheimer’s disease is the most common form of dementia and may contribute to 60–70% of cases. It has been proved that early diagnosis is key to promoting early and optimal management. However, the early stage of dementia is often overlooked and patients are typically diagnosed when the disease progresses to a more advanced stage. The objective of this contribution is to predict Alzheimer’s early stages, not only dementia itself. To carry out this objective, different types of SVM and CNN machine learning classifiers will be used, as well as two different feature selection algorithms: PCA and mRMR. The different experiments and their performance are compared when classifying patients from MRI images. The newness of the experiments conducted in this research includes the wide range of stages that we aim to predict, the processing of all the available information simultaneously and the Segmentation routine implemented in SPM12 for preprocessing. We will make use of multiple slices and consider different parts of the brain to give a more accurate response. Overall, excellent results have been obtained, reaching a maximum F1 score of 0.9979 from the SVM and PCA classifier.

Highlights

Wavelet transform [1] and its applications are a powerful tool in image processing.Since the rise of wavelet analysis in the early 1980s, it has been shown to be useful in many fields of applied mathematics, in signal and image processing.The recent advances in neural networks and transfer learning methods [2] have opened the door to discussions about the goodness and supremacy of neural networks over the traditional transforms in pattern recognition and classification.At the same time, Alzheimer’s disease has established itself as one of the great epidemics of the 21st century, being the most common case of dementia
Let us begin analysing the results from the mRMR tests, in which our main objective is to maximize F1 score
Despite the fact that the scores obtained using the custom convolutional neural network (CNN) are more than adequate, it still performs worse than our Support Vector Machine (SVM) classifier based on principal component analysis (PCA))

Summary

Introduction

Wavelet transform [1] and its applications are a powerful tool in image processing.Since the rise of wavelet analysis in the early 1980s, it has been shown to be useful in many fields of applied mathematics, in signal and image processing.The recent advances in neural networks and transfer learning methods [2] have opened the door to discussions about the goodness and supremacy of neural networks over the traditional transforms in pattern recognition and classification.At the same time, Alzheimer’s disease has established itself as one of the great epidemics of the 21st century, being the most common case of dementia. Wavelet transform [1] and its applications are a powerful tool in image processing. Since the rise of wavelet analysis in the early 1980s, it has been shown to be useful in many fields of applied mathematics, in signal and image processing. It has been proved to present intermediate stages before the severity of the dementia becomes moderate or serious. These stages can be noticeable and, on occasion, patients are even aware of their own cognitive impairment. Such symptoms can go unnoticed until the disease presents an advanced stage. Detecting and diagnosing early stages can be the key to provide patients with better prognosis and preventive treatments in order to improve their quality of life

Objectives

Methods

Results

Conclusion