Transfer Learning Based Prediction Model for Obstructive Sleep Apnea from Facial Depth Maps
Keywords:
Obstructive sleep apnea, VGG-19, deep learning.Abstract
Stress levels are rising at an alarming rate today as a direct result of the increased level of competition in both the educational and professional spheres. This stress is a contributing factor in the development of a wide variety of ailments, including obstructive sleep apnea. Relaxation of the tongue and the muscles that line the airway may cause obstructive sleep apnea (OSA), which occurs when there is a recurring blockage in the airway during sleep. Snoring, difficulty sleeping because of choking or gasping for breath, and waking up feeling exhausted are typical symptoms of obstructive sleep apnea (OSA). The OSA diagnosis is time-consuming and expensive, both financially and in terms of lost productivity. Because of this, a significant number of patients continue to go untreated and are uninformed of the nature of their illness. Through a depth map of human face scans, the application of deep learning algorithms is employed to identify the condition. In comparison to a standard 2-D colour picture, the depth map offers much more information on the morphology of the face. The traditional machine learning models did not succeed in producing the best possible results in terms of prediction and classification accuracy. Following the extraction of deep face map features using the proposed VGG-19 method and the subsequent training of both the algorithm and a module that was learned on the IMAGENET dataset, transfer learning is used to train the algorithm on OSA facial pictures. The deep learning algorithm known as VGG-19 is trained with the use of the photos from the 3D face scan. In order to predict OSA from fresh test photos, a trained model of VGG-19 is used.
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