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

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 m...

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Autores principales:	Alhanoof Althnian, Nada Almanea, Nourah Aloboud
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	jaundice healthcare smartphone sensor diagnosis machine learning deep learning Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/d31694ae48de4f7a93eeb3c537c35f4a
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spelling	oai:doaj.org-article:d31694ae48de4f7a93eeb3c537c35f4a2021-11-11T19:04:09ZNeonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning10.3390/s212170381424-8220https://doaj.org/article/d31694ae48de4f7a93eeb3c537c35f4a2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7038https://doaj.org/toc/1424-8220Neonatal 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.Alhanoof AlthnianNada AlmaneaNourah AloboudMDPI AGarticlejaundicehealthcaresmartphone sensordiagnosismachine learningdeep learningChemical technologyTP1-1185ENSensors, Vol 21, Iss 7038, p 7038 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	jaundice healthcare smartphone sensor diagnosis machine learning deep learning Chemical technology TP1-1185
spellingShingle	jaundice healthcare smartphone sensor diagnosis machine learning deep learning Chemical technology TP1-1185 Alhanoof Althnian Nada Almanea Nourah Aloboud Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning
description	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.
format	article
author	Alhanoof Althnian Nada Almanea Nourah Aloboud
author_facet	Alhanoof Althnian Nada Almanea Nourah Aloboud
author_sort	Alhanoof Althnian
title	Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning
title_short	Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning
title_full	Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning
title_fullStr	Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning
title_full_unstemmed	Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning
title_sort	neonatal jaundice diagnosis using a smartphone camera based on eye, skin, and fused features with transfer learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d31694ae48de4f7a93eeb3c537c35f4a
work_keys_str_mv	AT alhanoofalthnian neonataljaundicediagnosisusingasmartphonecamerabasedoneyeskinandfusedfeatureswithtransferlearning AT nadaalmanea neonataljaundicediagnosisusingasmartphonecamerabasedoneyeskinandfusedfeatureswithtransferlearning AT nourahaloboud neonataljaundicediagnosisusingasmartphonecamerabasedoneyeskinandfusedfeatureswithtransferlearning
_version_	1718431672197709824

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

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