Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation

Ornek, Ahmet Haydar; Savasci, Duygu; Ceylan, Murat; Soylu, Hanifi; Konak, Murat

Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation

Duygu SAVAŞCI, (Konya Teknik Üniversitesi, Elektrik-Elektronik Mühendisliği Bölümü, Konya, Türkiye)

Murat CEYLAN, (Konya Teknik Üniversitesi, Elektrik-Elektronik Mühendisliği Bölümü, Konya, Türkiye)

Ahmet Haydar ÖRNEK, (Konya Teknik Üniversitesi, Elektrik-Elektronik Mühendisliği Bölümü, Konya, Türkiye)

Murat KONAK, (Selçuk Üniversitesi, Tıp Fakültesi, Çocuk Sağlığı ve Hastalıkları Anabilim Dalı, Konya, Türkiye)

Hanifi SOYLU (Selçuk Üniversitesi, Tıp Fakültesi, Çocuk Sağlığı ve Hastalıkları Anabilim Dalı, Konya, Türkiye)

International Journal of Intelligent Systems and Applications in Engineering

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Yıl: 2020 Cilt: 8 Sayı: 1 Sayfa Aralığı: 28 - 36 Metin Dili: İngilizce İndeks Tarihi: 08-11-2020

Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation

Öz:

Monitoring temperature changes of infants in the neonatal intensive care unit is very important. Especially for premature andvery low birthweight infants, determining temperature changes in their skin immediately is extremely significant for follow-up processes.The development of medical infrared thermal imaging technologies provides accurate and contact-free measurement of bodytemperature. This method is used to detect thermal radiation emitted from the body to obtain skin temperature distributions. The purposeof this study is to develop an analysis system based on infrared thermal imaging to classify neonates who are healthy and suffering fromheart disease using their skin temperature distribution. In this study, 258 infrared thermograms obtained applying data augmentation on43 infrared thermograms captured from the Neonatal Intensive Care Unit were used. The following operations were performed: firstly,images were segmented to eliminate unnecessary details on the thermogram. Secondly, the features of the image were extractedapplying Discrete Wavelet Transform (DWT), Ridgelet Transform (RT), Curvelet Transform (CuT), and Contourlet Transform (CoT)which are multiresolution analysis methods. Finally, these features are classified as healthy and unhealthy using classification methodssuch as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF). The best results were obtained withSVM as 96.12% of an accuracy, 94.05% of a sensitivity and 98.28% of a specificity.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

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APA	Savasci D, Ceylan M, Ornek A, Konak M, Soylu H (2020). Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation. , 28 - 36.
Chicago	Savasci Duygu,Ceylan Murat,Ornek Ahmet Haydar,Konak Murat,Soylu Hanifi Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation. (2020): 28 - 36.
MLA	Savasci Duygu,Ceylan Murat,Ornek Ahmet Haydar,Konak Murat,Soylu Hanifi Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation. , 2020, ss.28 - 36.
AMA	Savasci D,Ceylan M,Ornek A,Konak M,Soylu H Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation. . 2020; 28 - 36.
Vancouver	Savasci D,Ceylan M,Ornek A,Konak M,Soylu H Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation. . 2020; 28 - 36.
IEEE	Savasci D,Ceylan M,Ornek A,Konak M,Soylu H "Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation." , ss.28 - 36, 2020.
ISNAD	Savasci, Duygu vd. "Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation". (2020), 28-36.

APA	Savasci D, Ceylan M, Ornek A, Konak M, Soylu H (2020). Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation. International Journal of Intelligent Systems and Applications in Engineering, 8(1), 28 - 36.
Chicago	Savasci Duygu,Ceylan Murat,Ornek Ahmet Haydar,Konak Murat,Soylu Hanifi Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation. International Journal of Intelligent Systems and Applications in Engineering 8, no.1 (2020): 28 - 36.
MLA	Savasci Duygu,Ceylan Murat,Ornek Ahmet Haydar,Konak Murat,Soylu Hanifi Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation. International Journal of Intelligent Systems and Applications in Engineering, vol.8, no.1, 2020, ss.28 - 36.
AMA	Savasci D,Ceylan M,Ornek A,Konak M,Soylu H Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation. International Journal of Intelligent Systems and Applications in Engineering. 2020; 8(1): 28 - 36.
Vancouver	Savasci D,Ceylan M,Ornek A,Konak M,Soylu H Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation. International Journal of Intelligent Systems and Applications in Engineering. 2020; 8(1): 28 - 36.
IEEE	Savasci D,Ceylan M,Ornek A,Konak M,Soylu H "Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation." International Journal of Intelligent Systems and Applications in Engineering, 8, ss.28 - 36, 2020.
ISNAD	Savasci, Duygu vd. "Heart Disease Detection from Neonatal Infrared Thermograms Using Multiresolution Features and Data Augmentation". International Journal of Intelligent Systems and Applications in Engineering 8/1 (2020), 28-36.