Table 1.
Comparison of related works with their reported results.
| Ref. | Feature Extraction | Method | Dataset | Result |
|---|---|---|---|---|
| [7] | Face skin (mean, standard deviation, skewness, kurtosis, energy, entropy) | K-Nearest Neighbours (KNN) | 120 random images from Google infant monitoring | Accuracy = 90–96% |
| [8] | Forehead skin (RGB) | Linear regression model | 64 images at Aalborg University Hospital in Denmark | Green sensitivity = 100%, specificity = 62% Blue sensitivity = 90%, specificity = 60% |
| [9] | Sternum and forehead skin (YCbCr and lab color spaces) | Ensemble of five regression algorithms (KNN, Least angle regression (LARS), LARS-Lasso Elastic Net, Support vector regression (SVR), Random forest (RF)) | 100 images collected from University of Washington Medical Center (UWMC) and the Roosevelt Pediatric Care Center | A linear correlation of 0.84 with TSB, with a mean error of 2.0 mg/dL |
| [10] | Forehead and sternum skin (Lab color spaces) | Matching | Standard set of serum bilirubin coloration on detection strips | Correlation = 0.93 |
| [11] | Forehead skin (RGB and Hue, Saturation, Intensity (HIS) values) | Regression | 113 images at Hafez and Shoushtari hospitals in Shiraz, Iran using a Samsung phone | Sensitivity = 68% Specificity = 92.3% |
| [12] | Sternum skin (YCbCr and lab color spaces) | Regression | 530 images of different races in US including African American, Hispanic, and Asian American | Sensitivity = 84.6% Specificity = 75.1% |
| [13] | Sternum and abdomen skin (Hue and Saturation values) | Regression | 35 images in Chennai, India | Sternum correlation = 0.6 Abdomen correlation = 0.55 |
| [14] | Abdomen skin (YCbCr, RGB, and lab color spaces) | KNN SVR |
80 image from Fırat University Faculty of Medicine, Neonatal Department in Turkey | KNN accuracy = 85% SVR accuracy = 75% |
| [15] | Soles, palm, forehead, and arm skin (RGB + diffuse reflectance spectra) | SVM | 20 images of Mexican infants | Sensitivity = 71.8% Specificity = 78.8% |
| [16] | Face, arms, feet and middle body skin (RGB) | Linear regression | 196 images at Firat university, Faculty of Medicine using an Android mobile phone or tablet | Accuracy = 92.5% |
| [17] | Eye (RGB) | Linear regression | 110 images at University College London Hospital captured using a Nikon D3200 camera | Correlation = 0.75 |
| [18] | Eye (sclera blue pixels) | Random forest regression | 70 images of adults eyes at University of Washington using an iPhone SE | Sensitivity = 89.7% Specificity = 96.8% |
| [19] | Eye (RGB) | Regression | 86 images at the UCH Neonatal Unit in London using a Nikon Dh3200 camera | Correlation = 0.71 |
| [20] | Eye | Diazo method with dichloroaniline (DCA) | 100 images at King Khalid Hospital at Al-Majma’ah, Saudi Arabia and Alpine Hospital, Gurgaon, India using a Samsung 10 | Sensitivity = 92.0% specificity = 75.6% |
| [21] | Eye (PCA to extract L, a, and b values per CIE lab color) | Artificial neuro-fuzzy inference system (ANFIS) | 420 images of adults’ eyes captured in fixed conditions using a 3CDD digital camera in aphotic housing made up of acrylic sheet | Accuracy = 90% |
| [22] | Eye (RGB) | Jaundice Eye Color Index Scleral-Conjunctival Bilirubin ((JECI-SCB) model and SCBxy model | 51 images from the UCL Hospital using an LG Nexus 5X smartphone | Correlation = 0.75 |
| [38] | Bilirubin sample strips (homomorphic filter and blue color intensity) | Correlation between actual and predicted bilirubin level | 8 images of bilirubin sample strips | Correlation coefficient increased from magnitude 0.5261 to magnitude 0.6974 after filtering |