FEDERATED DEEP LEARNING FOR THE DIAGNOSIS OF CEREBELLAR ATAXIA: PRIVACY PRESERVATION AND AUTO-CRAFTED FEATURE EXTRACTOR
ABSTARCT :
Cerebellar ataxia (CA) is concerned with the incoordination of movement caused by cerebellar dysfunction. Movements of the eyes, speech, trunk, and limbs are affected. Conventional machine learning approaches utilizing centralised databases have been used to objectively diagnose and quantify the severity of CA . Although these approaches achieved high accuracy,large scale deployment will require large clinics and raises privacy concerns. We use motion capture sensors during the performance of a standard neurological balance test obtained from four geographically separated clinics. The recurrence plot, melspectrogram, and poincaré plot are three transformation techniques explored.The recurrence plot, melspectrogram, and poincaré plot are three transformation techniques explored. The proposed scheme provides a practical solution with high diagnosis accuracy, removing the need for feature engineering and preserving data privacy for a large-scale deployment
EXISTING SYSTEM :
? The proposed scheme provides a practical solution with high diagnosis accuracy, removing the need for feature engineering and preserving data privacy for a large-scale deployment.
? we propose an image transformation-based approach to leverage the advantages of state-of-the-art deep learning with federated learning in diagnosing CA. We use motion capture sensors during the performance of a standard neurological balance test obtained from four geographically separated clinics. The recurrence plot, melspectrogram, and poincaré plot are three transformation techniques explored.
DISADVANTAGE :
Three fundamental problems in assessing ataxia are
? (i) classifying people as ataxics or non-ataxic controls;
? (ii) assessing severity with a regression model or classify the severity into sub-groups of low, moderate, and severe; and,
? (iii) classifying subjects ataxia phenotype to facilitate more specific therapeutic and rehabilitation programs
We evaluate three different image transformation techniques to use with DL and employ transfer learning to overcome the problem of limited datasets.
PROPOSED SYSTEM :
? we propose an image transformation-based approach to leverage the advantages of state-of-the-art deep learning with federated learning in diagnosing CA. We use motion capture sensors during the performance of a standard neurological balance test obtained from four geographically separated clinics. The recurrence plot, melspectrogram, and poincaré plot are three transformation techniques explored.
? The clinician’s experience and the inherent subjectiveness of the human decision-making procedure impact significantly on this assessment method.
ADVANTAGE :
? Researchers also used prism-equipped goggles to assess motor adaptation in ataxia
? In the balance domain, a cloud-based ML implementation operating on data from IMU based wearable sensors were used to quantify the severity of truncal ataxia .
? Interpolating and sliding a fixed-sized window with two second overlapping was used to pre-process the raw data. As recurrence plots are significantly reliable when the data length is longer than 1000 samples
? Most studies in CA have used traditional ML with hand-crafted feature extraction.
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