Applied a non-invasive method to blood glucose monitoring by hand skin image based on gray level co-occurrence matrix (GLCM) and artificial neural networks (ANN)

• Autorzy: Umar Usman , Syarif Syafruddin , Nurtanio Ingrid , Indrabayu

Identyfikatory

DOI

10.15199/48.2023.05.01

Warianty tytułu

PL

Zastosowane nieinwazyjnej metody monitorowania poziomu glukozy we krwi za pomocą obrazu skóry dłoni w oparciu o macierz współwystępowania poziomów szarości (GLCM) i sztuczne sieci neuronowe (ANN)

• Języki publikacji: EN

Abstrakty

EN

This study develops a non-invasive method to predict blood glucose through image processing. For investigation, several invasive images and glucose levels were taken. Types of samples based on age classification, 20-60 years. For accuracy and simple analysis, 37 images of participants as volunteers, samples were evaluated and investigated under the gray level co-occurrence matrix (GLCM). In this study, an artificial neural network (ANN) was used for all training and hand texture testing to detect glucose levels. The performance of this model is evaluated using Root Mean Square Error (RMSE) and correlation coefficient (r). Clarke Error Grid Analysis (EGA) variance was used in this investigation to determine the accuracy of the method. The results showed that the RMSE was close to the standard value, the regression coefficient was 0.95, and the Clarke EGA analysis: 81.08% was in the A .% zone. So that the blood glucose prediction model using the GLCM-ANN method is feasible to apply.

PL

Niniejsze badanie rozwija nieinwazyjną metodę przewidywania stężenia glukozy we krwi poprzez przetwarzanie obrazu. W celu zbadania wykonano kilka inwazyjnych obrazów i poziomów glukozy. Rodzaje próbek na podstawie klasyfikacji wiekowej, 20-60 lat. Dla dokładności i prostej analizy, 37 obrazów uczestników jako ochotników, próbki zostały ocenione i zbadane w ramach macierzy współwystępowania poziomu szarości (GLCM). W tym badaniu sztuczna sieć neuronowa (ANN) została wykorzystana do wszystkich testów treningu i tekstury dłoni w celu wykrycia poziomu glukozy. Wydajność tego modelu ocenia się za pomocą błędu średniokwadratowego (RMSE) i współczynnika korelacji (r). W tym badaniu zastosowano analizę wariancji siatki błędów Clarke'a (EGA) w celu określenia dokładności metody. Wyniki pokazały, że RMSE była zbliżona do wartości standardowej, współczynnik regresji wyniósł 0,95, a analiza Clarke EGA: 81,08% znajdowała się w strefie A.%. Aby model przewidywania stężenia glukozy we krwi przy użyciu metody GLCM-ANN był możliwy do zastosowania.

Słowa kluczowe

EN

glucose; non-invasive; GLCM; ANN

PL

stężenie glukozy we krw; macierz współwystępowania poziomu szarości; glukoza

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2023
• Tom: R. 99, nr 5
• Strony: 1--7
• Opis fizyczny: Bibliogr. 35 poz., rys., tab.

Twórcy

autor

Umar Usman

• Department of Electrical Engineering, Faculty of Engineering University of Hasanuddin Makassar Indonesia
• Department of Electro-medical Technology, Health Polytechnic Muhammadiyah Makassar Indonesia

• https://orcid.org/0000-0002-2027-6018

autor

Syarif Syafruddin

• Department of Electrical Engineering, Faculty of Engineering University of Hasanuddin Makassar Indonesia

• https://orcid.org/0000-0003-1285-1323

autor

Nurtanio Ingrid

• Department of Electrical Engineering, Faculty of Engineering University of Hasanuddin Makassar Indonesia

• https://orcid.org/0000-0002-3053-4201

autor

Indrabayu

• Department of Electrical Engineering, Faculty of Engineering University of Hasanuddin Makassar Indonesia

• https://orcid.org/0000-0003-2026-1809

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).