A comparative MRI-based study of ResNet-152 and novel deep learning approaches for early Alzheimer’s disease classification

Abstract

Alzheimer’s disease (AD) is the leading cause of dementia, making early-stage detection essential for timely intervention. Most prior studies have focused on binary AD classification, which limits sensitivity to disease progression. This study addressed this gap by evaluating whether tailored convolutional neural network (CNN) architectures could improve stage-aware classification using a publicly available magnetic resonance imaging (MRI) dataset containing 35,984 images across four diagnostic categories. The dataset underwent grayscale conversion, resizing, contrast enhancement, normalization, and class balancing prior to model development. Four models were trained and compared: ResNet-152, a custom multiclass CNN, a one-vs-one (OvO) model, and a one-vs-rest (OvR) model. Performance was measured using accuracy, precision, recall, F1 score, and confusion-matrix–based metrics. The custom multiclass CNN achieved the strongest performance, yielding the highest accuracy and balanced results across all evaluation metrics. These findings demonstrate the value of systematically comparing decomposition strategies for multi-stage Alzheimer’s detection and highlight the potential of the proposed approach to enhance early diagnostic support. Future work may incorporate multimodal inputs or hybrid architectures to improve sensitivity to subtle structural changes and further strengthen clinical applicability.