An efficient hybrid Deep Learning-Machine Learning method for diagnosing neurodegenerative disorders.

Abstract

A neurodegenerative illness known as Alzheimer's causes the loss of brain cells and the progressive atrophy of brain tissue. It badly affects a person’s normal life. However, if we are able to detect it early and treat it, most patients will be able to recover to some degree and lead a normal life with some dependence. Continuous clinical assessment is needed for diagnosing this type of disorder. Medical diagnosis today extensively relies on deep learning approaches. However, medical image data analysis has a lot of constraints. One of the major constraints faced during medical image analysis is data scarcity and data imbalance. In light of these concerns, the current study sets out to create a hybrid deep learning model that can effectively categorise various Alzheimer's disease variants using magnetic resonance imaging (MRI) data.

For solving data imbalance, first, we blur and sharpen all the images, and finally, we pass all these images along with the original images through a predefined CNN (Convolutional Neural Network) model that was trained using mnist weight for extracting features, then pass these features to an extra-tree classifier for feature reduction, and finally input these reduced features to a customised deep learning model. This work used different pre-trained models for extracting features for our customised DNN (Deep Neural Network) and compared those models with the cutting-edge model chosen as the base model. The results state that our proposed model, which was pre-trained using ResNet with a dropout concept, got the highest values of training accuracy (98.20) and validation accuracy (92.61). This model also effectively addresses the problem of overfitting.