Facemask Signs Sickness Via Density Neural Complex

Abstract

Facial and bodily (clinical gestalt) in Deep learning (DL) models improve the assessment of patients’ health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves classification of acutely ill patients. As in previous research in DL analysis of medical images, simulated or augmented data may be used to assess the usability  of  clinical  gestalt.  In  this  study,  we  developed  a  computer-aided  diagnosis  system  for  automatic Rug  sick    detection  using Facial  Cue of illness  images.  Acutely  sick  people were  rated  by naive observers  as  having paler  lips  and  skin,  a  more  swollen face, droopier corners of the mouth, more hanging eyelids, redder eyes, and less glossy and patchy skin, as well as appearing more tired. Our findings  suggest  that  facial  cues  associated  with  the  skin,  mouth  and  eyes  can  aid  in  the  detection  of  acutely  sick  and  potentially contagious people. We employed deep transfer learning to handle the scarcity of available data and designed a Neural Network (CNN) model along with the four transfer learning methods: VGG16, VGG19, InceptionV3, Xception and ResNet50. Where, in the existing methods ResNet101 is used that which did not got the proper accuracy and that tend to be improved. Hence the present method with other transfer learning methods is proposed. The proposed approach was evaluated  on publicly available Facial Cue of illness dataset.