Alzheimer's related dementia severity classification from magnetic resonance imaging using derivative-free optimization of convolutional neural network.

Magnetic resonance imaging based dementia severity assessment facilitates timely diagnosis of Alzheimer’s disease (AD) progression. While convolutional neural networks (CNNs) have shown effectiveness in classifying AD stages, their performance can be limited by high computational demands and class imbalance in training data. The article introduces a derivative-free optimization (DFO) approach by integrating evolutionary algorithms, Bayesian optimization, simulated annealing, neural architecture search, and pruning techniques to optimize both trainable parameters and network topology to design a lightweight CNN model (DAPA-CNN) for classifying brain MRI images into four Alzheimer-related Dementia stages (ARDS): non-Dementia (ND), mild Dementia (MD), very mild Dementia (VMD) and moderate Dementia (MoD). The proposed framework balances the dataset using Tomek links and deep SMOTE, and enhances model interpretability with class activation maps (CAMs). DAPA-CNN achieved an accuracy of 99.59% on the Alzheimer’s disease dataset (ADD), along with balanced precision (99.60%), sensitivity (99.66%), specificity (99.87%), and F1score (99.63%). across all classes. All class-wise dice and Jaccard indices and correlation metrics (Matthews correlation coefficient and Cohen’s Kappa) exceeded 0.99. Compared to a baseline CNN and contemporary architectures, DAPA-CNN reduces the number of parameters by 85.6%, processing time by 42.8%, and memory usage by 76.6%, making it suitable for resource-constrained clinical environments.