Adversarial attacks on medical imaging pose a critical threat to AI-guided diagnosis, as these invisible perturbations can cause catastrophic misclassifications. This research presents the Multi-model Adversarial Defense (MMAD) system, introducing novel algorithmic innovations for medical imaging protection, specifically targeting Magnetic Resonance Imaging (MRI). The proposed solution features a groundbreaking hybrid classifier integrating Vision Transformer, Convolutional Neural Network, and Spiking Neural Network through a novel attention-based fusion algorithm: α = Softmax(W_attn · [f_vit, f_cnn, f_snn]), where fused features f_fused = Σ α_i · f_i dynamically weight each model's contribution. This architecture incorporates auxiliary classifiers with weighted focal loss, achieving remarkable benchmarking results on MNIST dataset: 98% accuracy for FGSM, 96% for BIM, 90.4% for PGD, and 100% for clean images at epsilon=0.05. The two-phase purification framework introduces an innovative multi-branch output strategy: Purified Image = Main + 0.3 × Detail + 0.15 × Edge, utilizing a U-Net generator with dual attention mechanisms (channel+spatial) and self-attention at the bottleneck. Phase-two refinement employs a PatchGAN discriminator with spectral normalization, achieving peak PSNR of 34.61dB and SSIM of 0.9551. Unlike existing solutions, MMAD eliminates the accuracy-security trade-off while providing generalized defense against multiple white-box attacks (FGSM, BIM, PGD) at low perturbation intensities (ε=0.01-0.05). The medical-first algorithmic design preserves anatomical structures and diagnostic features, establishing new benchmarks for adversarial defense in healthcare AI systems, ultimately enhancing patient safety and diagnostic reliability.