基于卷积神经网络（CNN）混合特征和手工特征的MRI图像自动分析用于阿尔茨海默病阶段的早期预测

Automatic Analysis of MRI Images for Early Prediction of Alzheimer's Disease Stages Based on Hybrid Features of CNN and Handcrafted Features.

作者信息

Khalid Ahmed, Senan Ebrahim Mohammed, Al-Wagih Khalil, Al-Azzam Mamoun Mohammad Ali, Alkhraisha Ziad Mohammad

机构信息

Computer Department, Applied College, Najran University, Najran 66462, Saudi Arabia.

Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen.

出版信息

Diagnostics (Basel). 2023 May 8;13(9):1654. doi: 10.3390/diagnostics13091654.

DOI:10.3390/diagnostics13091654

PMID:37175045

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10178535/

Abstract

Alzheimer's disease (AD) is considered one of the challenges facing health care in the modern century; until now, there has been no effective treatment to cure it, but there are drugs to slow its progression. Therefore, early detection of Alzheimer's is vital to take needful measures before it develops into brain damage which cannot be treated. Magnetic resonance imaging (MRI) techniques have contributed to the diagnosis and prediction of its progression. MRI images require highly experienced doctors and radiologists, and the analysis of MRI images takes time to analyze each slice. Thus, deep learning techniques play a vital role in analyzing a huge amount of MRI images with high accuracy to detect Alzheimer's and predict its progression. Because of the similarities in the characteristics of the early stages of Alzheimer's, this study aimed to extract the features in several methods and integrate the features extracted from more than one method into the same features matrix. This study contributed to the development of three methodologies, each with two systems, with all systems aimed at achieving satisfactory accuracy for the detection of AD and predicting the stages of its progression. The first methodology is by Feed Forward Neural Network (FFNN) with the features of GoogLeNet and DenseNet-121 models separately. The second methodology is by FFNN network with combined features between GoogLeNet and Dense-121 models before and after high-dimensionality reduction of features using the Principal Component Analysis (PCA) algorithm. The third methodology is by FFNN network with combined features between GoogLeNet and Dense-121 models separately and features extracted by Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods called handcrafted features. All systems yielded super results in detecting AD and predicting the stages of its progression. With the combined features of the DenseNet-121 and handcrafted, the FFNN achieved an accuracy of 99.7%, sensitivity of 99.64%, AUC of 99.56%, precision of 99.63%, and a specificity of 99.67%.

摘要

阿尔茨海默病（AD）被认为是现代社会医疗保健面临的挑战之一；到目前为止，尚无有效的治愈方法，但有药物可减缓其进展。因此，阿尔茨海默病的早期检测对于在其发展为无法治疗的脑损伤之前采取必要措施至关重要。磁共振成像（MRI）技术有助于其诊断和进展预测。MRI图像分析需要经验丰富的医生和放射科医生，且分析每张切片都需要时间。因此，深度学习技术在高精度分析大量MRI图像以检测阿尔茨海默病并预测其进展方面发挥着至关重要的作用。由于阿尔茨海默病早期阶段特征存在相似性，本研究旨在采用多种方法提取特征，并将从多种方法中提取的特征整合到同一个特征矩阵中。本研究促成了三种方法的开发，每种方法包含两个系统，所有系统旨在实现令人满意的阿尔茨海默病检测准确率并预测其进展阶段。第一种方法是通过前馈神经网络（FFNN）分别结合GoogLeNet和DenseNet - 121模型的特征。第二种方法是通过FFNN网络，在使用主成分分析（PCA）算法对特征进行高维降维前后，结合GoogLeNet和Dense - 121模型之间的特征。第三种方法是通过FFNN网络，分别结合GoogLeNet和Dense - 121模型之间的特征以及通过离散小波变换（DWT）、局部二值模式（LBP）和灰度共生矩阵（GLCM）方法提取的特征，即手工特征。所有系统在检测阿尔茨海默病和预测其进展阶段方面均取得了优异结果。通过DenseNet - 121和手工特征的组合，FFNN实现了99.7%的准确率、99.64%的灵敏度、99.56%的AUC、99.63%的精确率和99.67%的特异性。