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

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.

Видео Prediction of Alzheimer’s Disease Progression Using Attention U-Net канала Sivakrishna Kondaveeti