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This study investigates advanced deep learning approaches for classifying AD from brain MRI, focusing on both predictive performance and resource efficiency. We evaluate a suite of convolutional neural network (CNN) architectures\u2014including VGG16, ResNet50V2, MobileNetV2, EfficientNetB0, a custom lightweight CNN \u2014 under diverse configurations of activation functions (Mish, GELU, Swish, PReLU) and optimizers (AdamW, AdaBelief, NovoGrad, Lion, and SGD with cosine decay). Lightweight Vision Transformers (ViTs), such as MobileViT, are also benchmarked for comparison. Ensemble strategies are explored through both soft-voting and trainable attention mechanisms. The attention-based ensemble achieved the highest classification performance with an F1-score of 0.9492, while an efficient CNN-VGG ensemble reduced model size and memory usage by over threefold with only a minor performance trade-off. The best lightweight transformer model, MobileViT-Small, achieved an F1-score of 0.927 with exceptionally low inference latency, making it suitable for edge deployment. 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