Leveraging lightweight CNN for multiclass classification of Alzheimer’s disease in comparison with machine learning and distinct transfer learning models

Alzheimer’s disease (AD) is a common neurological condition that causes brain cells to atrophy, progressively impairing a person’s ability to think and react independently. The possibility of reducing the growth of this mental illness increases with early identification. MRI is a key biomarker used for measuring disease progression and allows for noninvasive longitudinal assessments. The proposed work suggests a deep learning (DL) based lightweight CNN approach for early AD identification. The suggested model was assessed and validated using the ADNI dataset, which includes three different types of MRI, such as AD, MCI, and CN. Further, the proposed scheme consists of a comparison of distinct machine learning and transfer learning models, which include Alexnet, DenseNet201, GoogleNet, InceptionV3, ResNet18, ResNet50, ResNet101, SqueezeNet, VGG-16, VGG-19, and Xception. The lightweight CNN for the multiclass classification of AD MRI images proposes the best pre-trained model to accurately predict the patient’s stage. It is observed that the best accuracy (99%) was attained utilizing a lightweight CNN, whereas machine learning (98%) and transfer learning (96%) achieved lower results. Our model outperforms previous studies, reducing doctors’ burden and speeding decisions, but reliance on the ADNI dataset highlights the need for future research using diverse, generalizable datasets.