Identification of Thermal Abnormalities by Analysis of Abdominal Infrared Thermal Images of Neonatal Patients

Nur, Ruqia

doi:10.22215/etd/2014-10076

Cited by 3 publications

(2 citation statements)

References 86 publications

(142 reference statements)

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“…Necrotizing enterocolitis (NEC) disease which causes a crucial neonatal health problem that affects low-birth weight infants in NICU [9] has been studied by Nur [10]. According to her findings, unhealthy neonates' thermal asymmetries are higher than healthy neonates'.…”

Section: Related Workmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Classification of Medical Thermal Images of Neonates Using Class Activation Maps

Örnek¹,

Ceylan²

2021

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In order to determine the health status of the neonates, studies focus on either statistical behavior of the thermograms' temperature distributions, or just correct classifications of the thermograms. However, there exists always a lack of explain-ability for classification processes. Especially in the medical studies, doctors need explanations to assess the possible results of the decisions. Presenting our new study, how Convolutional Neural Networks (CNNs) decide the health status of neonates has been shown for the first time by using Class Activation Maps (CAMs). VGG16 which is one of the pre-trained models has been selected as a CNN model and the last layers of the VGG16 have been tuned according to CAMs. When the model was trained for 50 epochs, train-validation accuracies reached over 95% and test sensitivity-specificity were obtained as 80.701%-96.842% respectively. According to our findings, the CNN learns the temperature distribution of the body by mainly looking at the neck, armpit, and abdomen regions. The focused regions of the healthy babies are armpit and abdomen whereas of the unhealthy babies are neck and abdomen regions. Thus, we can say that the CNN focuses on dedicated regions to monitor the neonates and decides the health status of the neonates.

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Section: Related Workmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Classification of Medical Thermal Images of Neonates Using Class Activation Maps

Örnek¹,

Ceylan²

2021

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“…In 2013, the identification of thermal abnormalities of neonatal patients was presented by Ruqia [6]. She explained the importance of using temperature changes instead of the spatial distribution of temperature values.…”

Section: Introductionmentioning

confidence: 99%

Classification of Medical Thermograms Belonging Neonates by Using Segmentation, Feature Engineering and Machine Learning Algorithms

Örnek¹,

Ervural²,

Ceylan³

et al. 2020

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Monitoring and evaluating the skin temperature value are considerably important for neonates. A system detecting diseases without any harmful radiation in early stages could be developed thanks to thermography. This study is aimed at detecting healthy/unhealthy neonates in neonatal intensive care unit (NICU). We used 40 different thermograms belonging 20 healthy and 20 unhealthy neonates. Thermograms were exported to thermal maps, and subsequently, the thermal maps were converted to a segmented thermal map. Local binary pattern and fast correlation-based filter (FCBF) were applied to extract salient features from thermal maps and to select significant features, respectively. Finally, the obtained features are classified as healthy and unhealthy with decision tree, artificial neural networks (ANN), logistic regression, and random forest algorithms. The best result was obtained as 92.5% accuracy (100% sensitivity and 85% specificity). This study proposes fast and reliable intelligent system for the detection of healthy/unhealthy neonates in NICU.

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Explainable Features in Classification of Neonatal Thermograms

Örnek

Ceylan

2020

2020 28th Signal Processing and Communications Applications Conference (SIU)

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Identification of Thermal Abnormalities by Analysis of Abdominal Infrared Thermal Images of Neonatal Patients

Cited by 3 publications

References 86 publications

Explainable Artificial Intelligence (XAI): Classification of Medical Thermal Images of Neonates Using Class Activation Maps

Explainable Artificial Intelligence (XAI): Classification of Medical Thermal Images of Neonates Using Class Activation Maps

Classification of Medical Thermograms Belonging Neonates by Using Segmentation, Feature Engineering and Machine Learning Algorithms

Explainable Features in Classification of Neonatal Thermograms

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