Prediction of Alzheimer’s Disease Progression using Attention U-Net

As of now Alzheimer’s disease doesn’t have a permanent cure. It’s a progressive neurodegenerative disorder that worsens over time primarily affecting thinking, memory and behaviour. Our model outlays the idea of predicting Alzheimer’s disease progression for early diagnosis aiming to slow disease progression and manage symptoms using an attention U-net model. This is generally a robust deep learning framework that is efficient in image segmentation and classification tasks and ex- tracts intricate spatial details from medical or industrial imaging datasets that are critical for precise analysis and decision-making. Traditional CNN models struggle to retain spatial accuracy and contextual awareness in complex datasets. To tackle the constraint, we developed a model that can focus on relevant features while suppressing noise or irrelevant features. It includes advanced data pre-processing, followed by a specially optimized Attention U-Net model that efficiently provides high-accuracy segmentation. The architecture involves a new loss function developed for the class imbalance problem. This approach trains a model without hindering the general performance of classes that are poorly represented. Besides, it uses the loss function sparse categorical crossentropy for efficiency while avoiding additional computation time, and hence, it is practical to deploy even with big volumes of datasets. It represents a general framework that is easily scalable and applicable to most tasks, including medical image segmentation, industrial defect detection, and disease classification. With this network, it’s possible to provide detailed segmentation maps with high precision, thus becoming very effective in environments where clarity and reliability are crucial. The proposed system also offers potential grounds for integration with real-time processing capabilities, hence being fit for dynamic applications like medical diagnostics and autonomous systems. Generally, the exceptional capability of the model for accurate and efficient segmentation forms a broad practical applicability in a wide range of domains. This was reflected in the overall test accuracy of the system at 65.75%, while flapping in classification performance across target classes did occur, showed strong precision for specific classes but struggled with recall due to underrepresented categories. Based on this, the macro-average F1-score of 0.41 and weighted average F1-score of 0.64 underline that further optimization will be required for better recall and balancing predictions.