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Aim: Analyse the diabetes mellitus (DM) of a person through the facial skin region using vision diabetology. Diabetes mellitus is caused by persistent high blood glucose levels and related complications, which show variation in facial skin regions due to reduced blood flow in the facial arteries. Materials and Method. In this study, 200 facial images of diabetes patients with skin conditions such as Bell's palsy, rubeosis faciei, scleroderma, and vitiligo were collected from existing face videos. Moreover, face images are collected from diabetic persons in India. Viola Jones' face-detecting algorithm extracts face skin regions from a diabetic person's face image in video frames. The affected skin area on the diabetic person's face is detected using HSV colour model segmentation. The proposed multiwavelet transform convolutional neural network (MWTCNN) extracts the features for diabetic measurement from up- and downfacial scaled images of diabetic persons. Results: The existing deep learning models are compared with the proposed MWTCNN model, which provides the highest accuracy of 98.3%. Conclusion: The facial skin region-based diabetic measurement avoids pricking of the serum and is used for continuous glucose monitoring.
Asunto(s)
Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/complicaciones , Diabetes Mellitus Tipo 2/diagnóstico , Automonitorización de la Glucosa Sanguínea , Glucemia , Piel , CaraRESUMEN
In this paper, early detection of schizophrenia types such as hallucination and delusion propose through the high Q-factor of RADWT in EEG signal acquired during the cognitive task of the patient. The earlier diagnose obtains from the energy distribution of the EEG signal in the high resolution via optimum tuning in dilation factor,which influences the Q-factor, redundancy and ringing in the EEG signal. The early detection of type of schizophrenia prevents the illness progression and lifelong disease. In existing clinical trial, the psych clinician diagnose only the schizophrenia disease through the standard DSM screening question and Prodromal signs checklist according to the standard of American Psychiatric Association. Furthermore, clinician tries to diagnose the disease through brain imaging and EEG signal. However, procedure in the diagnosis of Schizophrenia possible only in the acute stage, minimum after 2 years of illness progression and still sub classification of the type of schizophrenia is a challenging task. In the proposed system, we acquire EEG signal during the three conditions such as reverse counting of the number, screening questions (DSM), and eye rest state with a distance of 1-m part of the clinician and patient to analyse cognitive behaviour. From the result of 25 patients EEG, signal during cognitive task show the different sub band energy pattern in RADWT to distinguish hallucination and delusion patient exactly for 21 patients and provide 84% of accuracy in sub-classification of type schizophrenia.