LAD: A Hybrid Deep Learning System for Benign Paroxysmal Positional Vertigo Disorders Diagnostic

نویسندگان

چکیده

Herein, we introduce “Look and Diagnose” (LAD), a hybrid deep learning-based system that aims to support doctors in the medical field for diagnosing effectively Benign Paroxysmal Positional Vertigo (BPPV) disorder. Given body postures of patient Dix-Hallpike lateral head turns test, visual information both eyes is captured fed into LAD analyzing classifying one six possible disorders which might be suffering from. The proposed consists two streams: (1) an RNN-based stream takes raw RGB images extract features optical flow each eye followed by ternary classification determine left/right posterior canal (PC) or other; (2) pupil detector detects when it classified as Non-PC classifies direction strength beating categorize types remaining four classes: xmlns:xlink="http://www.w3.org/1999/xlink">Geotropic BPPV (left right) xmlns:xlink="http://www.w3.org/1999/xlink">Apogeotropic right). Experimental results show with given patient, capable accurately disorder accuracy 91% on validation set. method can successfully classify 93% xmlns:xlink="http://www.w3.org/1999/xlink">Posterior Canal 95% disorder, paving potential research data.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2022

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2022.3215625