Neuro Vision AI for Reliable and Earlier Warning of Alzheimer's Disease

Alzheimer's disease is a very serious neurological condition. Memory loss and a progressive decrease in mental capabilities are what ensue. This AD impacts millions of people across the world. Deep emotional disturbances and social complications arise from this for patients as well as their families and friends. Prompt therapies that reduce disease progression and enhance quality of life depend on early and correct diagnosis. This work presents Neuro Vision AI, which uses MRI data to diagnose Alzheimer's disease stages. The system categorises instances into four categories: Non-Demented, Very Mildly Demented, Mildly Demented, and Moderately Demented. To enhance performance as well as reliability, Neuro Vision AI uses the EfficientNetB3 convolutional neural network, which is famous for its feature extraction skills. We use Bayesian optimisation to fine-tune hyperparameters, ensuring optimum accuracy and reducing the risk of overfitting. Advanced preprocessing approaches. For example, data augmentation and normalisation, enhance dataset diversity while boosting generalisation. Regularisation strategies like dropout layers and batch normalisation stabilise the model during training. The proposed system works well as it is able to achieve more than its initial target of 95% accuracy with a test accuracy of 98.67%. Neuro Vision AI demonstrates the potential of artificial intelligence in medical diagnostics that presents an efficient and scalable tool for early diagnosis and personalised therapy in the management of Alzheimer's disease.