Abstract:
Studies on visual attention of patients with Alzheimer's disease and dementia are a promising way for keeping track of an individual patients image recognition ability ov...Show MoreMetadata
Abstract:
Studies on visual attention of patients with Alzheimer's disease and dementia are a promising way for keeping track of an individual patients image recognition ability over time. This research seeks to expand upon the current applications of combining the Android operating system with TensorFlow by providing QA diagnostics alongside a visual question answering (VQA) platform for image analysis. This framework, Cognitive Visual Recognition Tracker (CVRT), provides an entry point by which the user can ask symptom-related inquiries or visual questions concerning any image of their choosing, and then receive cumulative metrics over time to better assess any diminishing cognitive ability (i.e. Alzheimer's patients). In this work, recurrent neural networks alongside semantic analysis and knowledge graphs are leveraged to provide an interactive VQA experience. One of the main objectives of CVRT is for physicians to be able to determine trends from patient data that could either be applicable to the individual patient, or to many patients if an aggregate is formed from many individual datasets. On an individual level, these metrics would provide a way for the physician to monitor daily cognitive capability, whereas on a grander scale, these joint datasets could be used to provide better overall treatment for the disease with the future inclusion of Predictive Analytics. The final contribution is an interactive metrics platform by which other users can assess the primary users cognitive capacity based on features of their questioning, and to then provide them with accurate trending or possible remediation plans based on their condition.
Date of Conference: 13-16 November 2017
Date Added to IEEE Xplore: 18 December 2017
ISBN Information: