Integration of 2D-CNN and LSTM Networks for Enhanced Image Processing and Prediction in Alzheimer’s Disease

Abstract

Abstract—The early diagnosis of Alzheimer’s disease remains a major challenge due to the complexity of magnetic resonance image interpretation and the limitations of existing diagnostic models. The slow memory loss associated with the gradual loss of thinking abilities, known as Alzheimer's disease, is the most common element of the illness. Effective early diagnosis is therefore essential to treatment, unfortunately, the traditional diagnostic procedure, which involves analyzing magnetic resonance images, is a complex process and prone to mistakes. This study aims to successfully merge these cognitive models with advanced deep learning techniques to enhance the diagnostic capabilities of Alzheimer’s disease using a fusion model with 3-dimensional convolutional neural networks and long short-term memory networks. The proposed approach uses three-dimensional convolutional neural networks to extract intricate features from volumetric magnetic resonance images, while long short-term memory networks analyze sequential data to identify key temporal patterns that indicate the progression of Alzheimer's disease. The dataset used in this study is the Alzheimer's Disease Neuroimaging Initiative dataset, which contains magnetic resonance images labeled into four categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. The dataset consists of 6,400 magnetic resonance images in total, split into training (70%), validation (15%), and testing (15%) sets. These outcomes demonstrate that the hybrid model improves predictive accuracy significantly over current benchmarks on this topic. This study highlights the importance of introducing deep learning models into clinical practice, thereby providing an efficient tool for early-stage Alzheimer’s disease diagnosis, ultimately improving patient outcomes through early and accurate intervention.