Amsavalli K, Suba Raja S Kanaga, Sudha S
Department of Artificial Intelligence and Data Science, Easwari Engineering College, Chennai, India.
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India.
Curr Alzheimer Res. 2025 Jun 18. doi: 10.2174/0115672050373821250612053943.
Alzheimer's disease (AD) is a leading cause of death, making early detection critical to improve survival rates. Conventional manual techniques struggle with early diagnosis due to the brain's complex structure, necessitating the use of dependable deep learning (DL) methods. This research proposes a novel RESIGN model is a combination of Res-InceptionSeg for detecting AD utilizing MRI images.
The input MRI images were pre-processed using a Non-Local Means (NLM) filter to reduce noise artifacts. A ResNet-LSTM model was used for feature extraction, targeting White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). The extracted features were concatenated and classified into Normal, MCI, and AD categories using an Inception V3-based classifier. Additionally, SegNet was employed for abnormal brain region segmentation.
The RESIGN model achieved an accuracy of 99.46%, specificity of 98.68%, precision of 95.63%, recall of 97.10%, and an F1 score of 95.42%. It outperformed ResNet, AlexNet, Dense- Net, and LSTM by 7.87%, 5.65%, 3.92%, and 1.53%, respectively, and further improved accuracy by 25.69%, 5.29%, 2.03%, and 1.71% over ResNet18, CLSTM, VGG19, and CNN, respectively.
The integration of spatial-temporal feature extraction, hybrid classification, and deep segmentation makes RESIGN highly reliable in detecting AD. A 5-fold cross-validation proved its robustness, and its performance exceeded that of existing models on the ADNI dataset. However, there are potential limitations related to dataset bias and limited generalizability due to uniform imaging conditions.
The proposed RESIGN model demonstrates significant improvement in early AD detection through robust feature extraction and classification by offering a reliable tool for clinical diagnosis.
阿尔茨海默病（AD）是主要的死亡原因之一，因此早期检测对于提高生存率至关重要。由于大脑结构复杂，传统的手工技术在早期诊断方面存在困难，这就需要使用可靠的深度学习（DL）方法。本研究提出了一种新颖的RESIGN模型，它是Res-InceptionSeg的组合，用于利用MRI图像检测AD。
使用非局部均值（NLM）滤波器对输入的MRI图像进行预处理，以减少噪声伪影。使用ResNet-LSTM模型进行特征提取，目标是白质（WM）、灰质（GM）和脑脊液（CSF）。提取的特征进行拼接，并使用基于Inception V3的分类器分为正常、轻度认知障碍（MCI）和AD类别。此外，使用SegNet进行异常脑区分割。
RESIGN模型的准确率达到99.46%，特异性为98.68%，精确率为95.63%，召回率为97.10%，F1分数为95.42%。它分别比ResNet、AlexNet、DenseNet和LSTM高出7.87%、5.65%、3.92%和1.53%，并且分别比ResNet18、CLSTM、VGG19和CNN进一步提高了25.69%、5.29%、2.03%和1.71%的准确率。
时空特征提取、混合分类和深度分割的集成使得RESIGN在检测AD方面高度可靠。五折交叉验证证明了其稳健性，并且其性能在ADNI数据集上超过了现有模型。然而，由于数据集偏差以及成像条件统一导致的泛化性有限，存在潜在的局限性。
所提出的RESIGN模型通过强大的特征提取和分类，在早期AD检测方面取得了显著改进，为临床诊断提供了可靠的工具。
