Early prediction of Alzheimer's disease using convolutional neural network: a review

Abstract

In this paper, a comprehensive review on Alzheimer's disease (AD) is carried out, and an exploration of the two machine learning (ML) methods that help to identify the disease in its initial stages. Alzheimer's disease is a neurocognitive disorder occurring in people in their early onset. This disease causes the person to suffer from memory loss, unusual behavior, and language problems. Early detection is essential for developing more advanced treatments for AD. Machine learning (ML), a subfield of Artificial Intelligence (AI), uses various probabilistic and optimization techniques to help computers learn from huge and complicated data sets. To diagnose AD in its early stages, researchers generally use machine learning. The survey provides a broad overview of current research in this field and analyses the classification methods used by researchers working with ADNI data sets. It discusses essential research topics such as the data sets used, the evaluation measures employed, and the machine learning methods used. Our presentation suggests a model that helps better understand current work and highlights the challenges and opportunities for innovative and useful research. The study shows which machine learning method holds best for the ADNI data set. Therefore, the focus is given to two methods: the 18-layer convolutional network and the 3D convolutional network. Hence, CNNs with multi-layered fetch more accurate results as compared to 3D CNN. The work also contributes to the use of the ADNI data set, where the classification of training and testing samples is divided with such a number that brings the highest accuracy achieved with 18-layer CNN. The work concentrates on the early prediction of Alzheimer's disease with machine learning methods. Thus, the accuracy achieved is 98% for 18-layer CNN.

Application of Convolutional Neural Networks for Early Detection and Classification of Alzheimer's disease from MRI Images

“…We performed a framework look to optimize hyperparameters for each calculation utilizing the approval set [14]. The execution of each demonstration was assessed utilizing measurements such as exactness, affectability, specificity, and region beneath the collector working characteristic bend (AUC-ROC) on the autonomous test set.…”

An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning

“…The grad-CAM method leverages gradients between the classification score and the ultimate convolutional feature map to identify specific regions within an input image that exert the greatest impact on the classification score. The significance of these areas in influencing the final score is heightened in locations where the gradient is pronounced [40]. When we focus this on the images, it detects their regions with the help of the accuracy they achieved.…”

Certain Studies on Alzheimer's disease: A Comprehensive Review

“…Overfitting, data quality, interpretability, transparency, and reproducibility are some of these problems. The research conducted in [26] significantly improves the use of the ADNI dataset by dividing training and testing samples to maximize accuracy using an 18layer CNN. The study aims to use machine learning techniques to predict AD in advance.…”