Alzheimer's Disease (AD) is a progressive neurological disorder that can result in significant cognitive impairment and dementia. Accurate and timely diagnosis is essential for effective treatment and management of this disease. In this study, we proposed two low-parameter Convolutional Neural Networks (CNNs), IR-BRAINNET and Modified-DEMNET, designed to detect the early stages of AD accurately. We also introduced an ensemble model that averages their outputs to reduce variance across the CNNs and enhance AD detection. Both CNNs are trained, and all models are evaluated using a Magnetic Resonance Imaging (MRI) dataset from the Kaggle database. The dataset includes images of four stages of dementia, with an uneven class distribution. To mitigate challenges stemming from the inherent imbalance in the dataset, we employed the Synthetic Minority Over-sampling Technique (SMOTE) to generate additional instances for minority classes. In the NO-SMOTE scenario, despite the imbalanced distribution, the ensemble model achieved 98.28% accuracy, outperforming IR-BRAINNET (97.26%) and Modified-DEMNET (95.54%), with Wilcoxon p-values of 2.9e-3 and 5.20e-6, respectively, indicating significant improvement in correct predictions through the use of the average function. In the SMOTE scenario, the ensemble model achieved 99.92% accuracy (1.64% improvement over NO-SMOTE), IR-BRAINNET reached 99.80% (2.54% improvement), and Modified-DEMNET attained 99.72% (4.18% improvement). Based on the experimental findings, averaging the models' outputs enhanced AD diagnosis in both scenarios, while the diversity in the dataset introduced by SMOTE-generated instances significantly improved performance. Furthermore, the compact models we proposed outperformed those from previous studies, even in the presence of an imbalanced distribution.