Dhavamani Logeshwari, Joshi Sagar Vasantrao, Kothapalli Pavan Kumar Varma, Elangovan Muniyandy, Putchanuthala Ramesh Babu, Senthamil Selvan Ramasamy
Department of Information Technology, St Joseph's Institute of Technology, Chennai, Tamil Nadu, India.
Department of Electronics and Telecommunication Engineering, Nutan Maharashtra Institute of Engineering and Technology, Pune, Maharashtra, India.
Microsc Res Tech. 2025 Apr;88(4):1115-1127. doi: 10.1002/jemt.24727. Epub 2024 Dec 20.
The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, interpretability, ethical integration, population variety, and picture standardization must be addressed using deep learning for the functional magnetic resonance imaging (MRI) classification of AD in order to guarantee a trustworthy and practical therapeutic application. In this manuscript Classifying AD using a finite basis physics neural network (CAD-FBPINN) is proposed. Initially, images are collected from AD Neuroimaging Initiative (ADNI) dataset. The images are fed to Pre-processing segment. During the preprocessing phase the reverse lognormal Kalman filter (RLKF) is used to enhance the input images. Then the preprocessed images are given to the feature extraction process. Feature extraction is done by Newton-time-extracting wavelet transform (NTEWT), which is used to extract the statistical features such as the mean, kurtosis, and skewness. Finally the features extracted are given to FBPINNs for Classifying AD such as early mild cognitive impairment (EMCI), AD, mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), normal control (NC), and subjective memory complaints (SMCs). In General, FBPINN does not express adapting optimization strategies to determine optimal factors to ensure correct AD classification. Hence, sea-horse optimization algorithm (SHOA) to optimize FBPINN, which accurately classifies AD. The proposed technique implemented in python and efficacy of the CAD-FBPINN technique is assessed with support of numerous performances like accuracy, precision, Recall, F1-score, specificity and negative predictive value (NPV) is analyzed. Proposed CAD-FBPINN method attain 30.53%, 23.34%, and 32.64% higher accuracy; 20.53%, 25.34%, and 29.64% higher precision; 20.53%, 25.34%, and 29.64% higher NP values analyzed with the existing for Classifying AD Stages through Brain Modifications using FBPINNs Optimized with sea-horse optimizer. Then, the effectiveness of the CAD-FBPINN technique is compared to other methods that are currently in use, such as AD diagnosis and classification using a convolution neural network algorithm (DC-AD-AlexNet), Predicting diagnosis 4 years before Alzheimer's disease incident (PDP-ADI-GCNN), and Using the DC-AD-AlexNet convolution neural network algorithm, diagnose and classify AD.
在阿尔茨海默病（AD）病程中，淀粉样斑块、神经原纤维缠结、突触功能障碍和神经元死亡等病变会逐渐累积，导致认知能力下降和功能残疾。为确保功能性磁共振成像（MRI）对AD分类在治疗应用中可靠且实用，必须通过深度学习解决数据集质量、可解释性、伦理整合、人群多样性和图像标准化等挑战。本文提出了一种基于有限基物理神经网络的AD分类方法（CAD-FBPINN）。首先，从AD神经影像倡议（ADNI）数据集中收集图像。这些图像被输入到预处理部分。在预处理阶段，使用逆对数正态卡尔曼滤波器（RLKF）增强输入图像。然后，将预处理后的图像送入特征提取过程。特征提取通过牛顿时间提取小波变换（NTEWT）完成，该变换用于提取均值、峰度和偏度等统计特征。最后，将提取的特征输入到FBPINN中对AD进行分类，如早期轻度认知障碍（EMCI）、AD、轻度认知障碍（MCI）、晚期轻度认知障碍（LMCI）、正常对照（NC）和主观记忆障碍（SMC）。一般来说，FBPINN没有表达出适应优化策略来确定最佳因素以确保正确的AD分类。因此，采用海马优化算法（SHOA）对FBPINN进行优化，从而准确地对AD进行分类。该技术用Python实现，并通过准确性、精确性、召回率、F1分数、特异性和阴性预测值（NPV）等多种性能指标评估CAD-FBPINN技术的有效性。所提出的CAD-FBPINN方法在通过海马优化器优化的FBPINN对AD阶段进行分类时，与现有方法相比，准确率分别提高了30.53%、23.34%和32.64%；精确率分别提高了20.53%、25.34%和29.64%；NP值分别提高了20.53%、25.34%和29.64%。然后，将CAD-FBPINN技术的有效性与目前正在使用的其他方法进行比较，如使用卷积神经网络算法进行AD诊断和分类（DC-AD-AlexNet）、在阿尔茨海默病发病前4年预测诊断（PDP-ADI-GCNN）以及使用DC-AD-AlexNet卷积神经网络算法进行AD诊断和分类。
