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

Logeshwari Dhavamani; Sagar Vasantrao Joshi; Pavan Kumar Varma Kothapalli; Muniyandy Elangovan; Ramesh Babu Putchanuthala; Ramasamy Senthamil Selvan

doi:10.1002/jemt.24727

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

Microsc Res Tech. 2024 Dec 20. doi: 10.1002/jemt.24727. Online ahead of print.

Authors

Logeshwari Dhavamani¹, Sagar Vasantrao Joshi², Pavan Kumar Varma Kothapalli³, Muniyandy Elangovan⁴, Ramesh Babu Putchanuthala⁵, Ramasamy Senthamil Selvan⁶

Affiliations

¹ Department of Information Technology, St Joseph's Institute of Technology, Chennai, Tamil Nadu, India.
² Department of Electronics and Telecommunication Engineering, Nutan Maharashtra Institute of Engineering and Technology, Pune, Maharashtra, India.
³ Department of Computer Science and Engineering, Sagi Rama KrishnamRaju Engineering College, Bhimavaram, Andhra Pradesh, India.
⁴ Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
⁵ Associate Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, India.
⁶ Department of Electronics and Communication Engineering, Annamacharaya Institute of Technology and Sciences, Tirupati, Andhra Pradesh, India.

PMID: 39704389
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.

Keywords: Alzheimer's disease; Newton‐time‐extracting wavelet transform; finite basis physics‐informed neural networks; reverse lognormal Kalman filter; sea‐horse optimization algorithm.