Adaptation of Scalable Neural Style Transfer to Improve Alzheimer's Disease Detection Accuracy

Abstract

Creative augmentation methods in medical imaging, particularly in diagnosing Alzheimer’s disease, is a breakthrough approach in the current medical field. Alzheimer’s disease, a condition that causes the gradual deterioration of cognitive abilities, presents considerable difficulties in accurately diagnosing and interpreting brain imaging, particularly in the early stages. Neural-enhance Style Transfer (NST), once recognized in the creative field for its capacity to combine the styles of many images, is currently being adapted to improve the clarity and comprehensibility of brain scans used to diagnose Alzheimer’s disease. The scalability of this technology is incredibly revolutionary in managing the immense amount of neuroimaging data. In addition, this method also includes the transfer of stylistic characteristics from high-resolution, annotated brain MRIs to broader sets of standard scans, which often need to be clarified and defined. Such enhancement greatly enhances important characteristics, such as brain networks and regions of degeneration, which are essential for the early identification of Alzheimer’s disease. An exemplary use of NST in this field has shown a significant enhancement in the discernibility of brain microstructures, vital for early diagnosis of Alzheimer’s disease. This improvement has resulted in a considerable rise of over 25% in the accuracy of detecting first pathological alterations. This technological progress not only assists in the prompt and precise identification of medical conditions but also tackles the difficulty of effectively handling the increasing amount of neurological imaging data.