Detecting jaundice by using digital image processing

Castro‐Ramos, J.; Toxqui-Quitl, C.; Manriquez, F. Villa; Orozco-Guillen, E.; Padilla-Vivanco, Alfonso; Sánchez-Escobar, Jj.

doi:10.1117/12.2041354

Cited by 7 publications

(13 citation statements)

References 4 publications

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“…It is caused by excessive bilirubin in the blood, which results in yellow pigmentation in the skin, eye, or pale stools of the neonate [6]. A wide range of efforts exist in the literature that analyze images of neonates' eyes, skin, or pale stool to recognize jaundice [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Further, there exist a few efforts that do so using numerical data, such as [34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

“…Further, we summarize the results of the most relevant works in Table 1. For skin-based diagnosis, related efforts have used different parts of a neonate's body such as face [7], forehead [8][9][10][11]15], sternum [9,10,12,13], abdomen [13,14], or multiple body parts such as sole, palm, and arm [15] and face, arms, feet, and middle body [16]. The authors adopted varying methods for feature extraction such as mean, standard deviation, skewness, kurtosis, energy, and entropy [7], YCbCr and lab color spaces [9,12,14], RGB [11,12,[14][15][16], hue and saturation values [11,13], and diffuse reflection spectra features [15].…”

Section: Related Workmentioning

confidence: 99%

“…Existing works have used images of two different parts of the neonate's body, namely the neonate's skin and eye. For the former, researchers have used images of a neonate's face [7], forehead [8][9][10][11], sternum [9,10,12,13], abdomen [13,14], or multiple body parts such as sole, palm, forehead, and arm [15], and face, arms, feet, and middle body [16]. For the latter, researchers have used an eye's sclera for diagnosis [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning

Althnian

Almanea

Aloboud

2021

Sensors

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Neonatal jaundice is a common condition worldwide. Failure of timely diagnosis and treatment can lead to death or brain injury. Current diagnostic approaches include a painful and time-consuming invasive blood test and non-invasive tests using costly transcutaneous bilirubinometers. Since periodic monitoring is crucial, multiple efforts have been made to develop non-invasive diagnostic tools using a smartphone camera. However, existing works rely either on skin or eye images using statistical or traditional machine learning methods. In this paper, we adopt a deep transfer learning approach based on eye, skin, and fused images. We also trained well-known traditional machine learning models, including multi-layer perceptron (MLP), support vector machine (SVM), decision tree (DT), and random forest (RF), and compared their performance with that of the transfer learning model. We collected our dataset using a smartphone camera. Moreover, unlike most of the existing contributions, we report accuracy, precision, recall, f-score, and area under the curve (AUC) for all the experiments and analyzed their significance statistically. Our results indicate that the transfer learning model performed the best with skin images, while traditional models achieved the best performance with eyes and fused features. Further, we found that the transfer learning model with skin features performed comparably to the MLP model with eye features.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning

Althnian

Almanea

Aloboud

2021

Sensors

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“…Supervised classification requires training set to find the best before continue and compare with others data. Unsupervised classification is a method which examines a large number of unknown pixels and divides into a number of classes based on natural grouping in image value [4].…”

Section: Related Workmentioning

confidence: 99%

Jaundice Detection System

Raju¹

2019

IJATCSE

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Jaundice or neonatal jaundice is a medical term that describes an increasing amount of bilirubin in the body which makes the eyes, skin and whole body turning into yellow. Bilirubin is a yellow pigment in the body that release in the body after the birth of the baby. "Four out of five babies get jaundice". With approximately 8,000 babies born every day in Malaysia, 6,400 babies get jaundice. The proposed research includes a process for detection of jaundice based on the yellow skin colour. The detection will use the image processing technique of colour feature to extract and enhance the image to compare with the normal skin. It also classifies the level of the yellow colour using Color Detection Method which it classifies whether jaundice in a mild, severe or normal condition.

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“…Invasive blood sampling is painful and stressful for the neonates, resulting in blood loss and an increased risk of osteomyelitis [5][6][7] and infection at the site of sampling. [9][10][11] The system is not capable of making a quantitative estimation of bilirubin and is not portable either. 5 In the case of neonates, the possible alternative for invasive blood sampling is a transcutaneous bilirubinometer (BiliChek and JM-103 are the commercial versions of the device) that provides instantaneous cutaneous bilirubin concentration (TcB).…”

Section: Introductionmentioning

confidence: 99%

Development and optimization of a noncontact optical device for online monitoring of jaundice in human subjects

et al. 2015

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Jaundice is one of the notable markers of liver malfunction in our body, revealing a significant rise in the concentration of an endogenous yellow pigment bilirubin. We have described a method for measuring the optical spectrum of our conjunctiva and derived pigment concentration by using diffused reflection measurement. The method uses no prior model and is expected to work across the races (skin color) encompassing a wide range of age groups. An optical fiber-based setup capable of measuring the conjunctival absorption spectrum from 400 to 800 nm is used to monitor the level of bilirubin and is calibrated with the value measured from blood serum of the same human subject. We have also developed software in the LabVIEW platform for use in online monitoring of bilirubin levels in human subjects by nonexperts. The results demonstrate that relative absorption at 460 and 600 nm has a distinct correlation with that of the bilirubin concentration measured from blood serum. Statistical analysis revealed that our proposed method is in agreement with the conventional biochemical method. The innovative noncontact, low-cost technique is expected to have importance in monitoring jaundice in developing/underdeveloped countries, where the inexpensive diagnosis of jaundice with minimally trained manpower is obligatory.

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Detecting jaundice by using digital image processing

Cited by 7 publications

References 4 publications

Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning

Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning

Jaundice Detection System

Development and optimization of a noncontact optical device for online monitoring of jaundice in human subjects

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