Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network

• Published in: Microscopy research and technique Vol. 88; no. 4; pp. 1115 - 1127
• Main Authors: Dhavamani, Logeshwari, Joshi, Sagar Vasantrao, Kothapalli, Pavan Kumar Varma, Elangovan, Muniyandy, Putchanuthala, Ramesh Babu, Senthamil Selvan, Ramasamy
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2025; Wiley Subscription Services, Inc
• Subjects: Algorithms; Alzheimer Disease - classification; Alzheimer Disease - diagnosis; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Amyloid; Artificial neural networks; Brain - diagnostic imaging; Brain - pathology; Classification; Cognitive ability; Cognitive Dysfunction - classification; Cognitive Dysfunction - diagnostic imaging; Convolution; Datasets; Deep Learning; Diagnosis; Effectiveness; Ethical standards; Feature extraction; finite basis physics‐informed neural networks; Functional magnetic resonance imaging; Horses; Humans; Image Processing, Computer-Assisted - methods; Impairment; Kalman filters; Kurtosis; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Neurofibrillary tangles; Neuroimaging; Neuroimaging - methods; Newton‐time‐extracting wavelet transform; Optimization; Physics; reverse lognormal Kalman filter; sea‐horse optimization algorithm; Senile plaques; Wavelet transforms; sea‐horse optimization algorithm; finite basis physics‐informed neural networks; reverse lognormal Kalman filter; Newton‐time‐extracting wavelet transform; Alzheimer's disease
• ISSN: 1059-910X; 1097-0029; 1097-0029
• DOI: 10.1002/jemt.24727

ABSTRACT 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. For Alzheimer's disease (AD) classification, MRI images from the ADNI dataset are first preprocessed using a reverse lognormal Kalman filter (RLKF) to enhance image quality. Key statistical features are then extracted with the Newton‐time‐extracting wavelet transform (NTEWT). A finite basis physics‐informed neural network (FBPINN), optimized with the sea‐horse optimization algorithm (SHOA), is employed for classifying AD stages. This approach effectively classifies stages including EMCI, AD, MCI, LMCI, NC, and SMC.