Automatic Gestational Age Estimation Tool using Baby Feet Photographs

Medically the newborns are classified into five classes namely, extremely premature, very premature, moderately premature, term and late. Both premature and post-mature babies have poor survival rates because of their vulnerability to microbial infections and low immunity to many life threatening di...

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Main Author:	Singhal, Gautam
Format:	Dissertation (University of Nottingham only)
Language:	English
Published:	2016
Online Access:	http://eprints.nottingham.ac.uk/39163/ http://eprints.nottingham.ac.uk/39163/1/Gautam%20Singhal%204245504.pdf

id	nottingham-39163
recordtype	eprints
spelling	nottingham-391632017-10-19T17:36:00Z http://eprints.nottingham.ac.uk/39163/ Automatic Gestational Age Estimation Tool using Baby Feet Photographs Singhal, Gautam Medically the newborns are classified into five classes namely, extremely premature, very premature, moderately premature, term and late. Both premature and post-mature babies have poor survival rates because of their vulnerability to microbial infections and low immunity to many life threatening diseases. The more premature or post-mature the newborn is, the less are the chances of survival of the newborn. These lives can be saved by identifying the newborn’s gestational age which can be utilised to find the best treatment available for that age. There are some traditional methods to find the gestational age of the newborns which can be used both prenatally or postnatally. But usually these methods either require expensive machinery and/or well trained personnel who are not always easily available in developing countries. This is why neonatal mortality rate is high among developing nations. This project aims to create a model which can automatically estimate the gestational age of infants from the photographs of their foot. This model would be useful pertinently for the countries with poor or inadequate medical facilities as it voids the requirements for any expensive machinery or trained personnel. This project analysed the subject by making extensive use of machine learning and deep learning. The image classification shows promising results that is based on Artificial Neural Network and Convolutional Neural Network architectures. In order to find the best fit model for this real world issue, different architectures were trained and tested to classify the newborns according to their gestational age. ANN architectures have showed an encouraging performance for the classification. However, CNN architectures showed poor results and would require more data for better results of classification. 2016-12-14 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en http://eprints.nottingham.ac.uk/39163/1/Gautam%20Singhal%204245504.pdf Singhal, Gautam (2016) Automatic Gestational Age Estimation Tool using Baby Feet Photographs. [Dissertation (University of Nottingham only)]
repository_type	Digital Repository
institution_category	Local University
institution	University of Nottingham Malaysia Campus
building	Nottingham Research Data Repository
collection	Online Access
language	English
description	Medically the newborns are classified into five classes namely, extremely premature, very premature, moderately premature, term and late. Both premature and post-mature babies have poor survival rates because of their vulnerability to microbial infections and low immunity to many life threatening diseases. The more premature or post-mature the newborn is, the less are the chances of survival of the newborn. These lives can be saved by identifying the newborn’s gestational age which can be utilised to find the best treatment available for that age. There are some traditional methods to find the gestational age of the newborns which can be used both prenatally or postnatally. But usually these methods either require expensive machinery and/or well trained personnel who are not always easily available in developing countries. This is why neonatal mortality rate is high among developing nations. This project aims to create a model which can automatically estimate the gestational age of infants from the photographs of their foot. This model would be useful pertinently for the countries with poor or inadequate medical facilities as it voids the requirements for any expensive machinery or trained personnel. This project analysed the subject by making extensive use of machine learning and deep learning. The image classification shows promising results that is based on Artificial Neural Network and Convolutional Neural Network architectures. In order to find the best fit model for this real world issue, different architectures were trained and tested to classify the newborns according to their gestational age. ANN architectures have showed an encouraging performance for the classification. However, CNN architectures showed poor results and would require more data for better results of classification.
format	Dissertation (University of Nottingham only)
author	Singhal, Gautam
spellingShingle	Singhal, Gautam Automatic Gestational Age Estimation Tool using Baby Feet Photographs
author_facet	Singhal, Gautam
author_sort	Singhal, Gautam
title	Automatic Gestational Age Estimation Tool using Baby Feet Photographs
title_short	Automatic Gestational Age Estimation Tool using Baby Feet Photographs
title_full	Automatic Gestational Age Estimation Tool using Baby Feet Photographs
title_fullStr	Automatic Gestational Age Estimation Tool using Baby Feet Photographs
title_full_unstemmed	Automatic Gestational Age Estimation Tool using Baby Feet Photographs
title_sort	automatic gestational age estimation tool using baby feet photographs
publishDate	2016
url	http://eprints.nottingham.ac.uk/39163/ http://eprints.nottingham.ac.uk/39163/1/Gautam%20Singhal%204245504.pdf
first_indexed	2018-09-06T12:57:22Z
last_indexed	2018-09-06T12:57:22Z
_version_	1610862971432468480

Automatic Gestational Age Estimation Tool using Baby Feet Photographs

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