Efficient Deep Learning Algorithm for Alzheimer's Disease Diagnosis using Retinal Images

Alzheimer's Disease (AD) is crucial to detect in the early stage and recent studies suggest diagnosing AD using Machine Learning. We propose an efficient AD diagnosis Deep Learning algorithm using retinal fundus images. The proposed method employs MobileNetV3 as a backbone, and an Attention mechanism is applied. However, we modified the backbone structure into U-Net-like architecture to perform better. Furthermore, we modified the conventional Attention mechanism to the Weighted Attention mechanism. The masking-adding process has been applied in the training method of the model. Therefore, our model has 8.4% better classification performance than the current state-of-the-art model by achieving 0.929 AUC in the validation set.