Abstract
Infrared thermal imaging and deep learning provide intelligent monitoring systems that detect diseases in early phases. However, deep learning models require thousands of labeled images to be effectively trained from scratch. Since such a dataset cannot be collected from a neonatal intensive care unit (NICU), deep transfer learning models and methods were used for the first time in this study to classify neonates in the NICU as healthy and unhealthy. When nine different pre-trained models (VGG16, VGG19, Xception, ResNet101, ResNet50, Inceptionv3, InceptionResNetv2, MobileNet and DenseNet201) and two different classification methods (Multilayer Perceptrons and Support Vector Machines (SVMs)) were compared, best results were obtained as 100.00% specificity, sensitivity and accuracy with VGG19, and SVMs. This study proposes highest classification performance when comparing other studies that detect health status of neonates.
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References
Abbas A, Abdelsamea MM, Gaber MM (2020) Detrac: transfer learning of class decomposed medical images in convolutional neural networks. IEEE Access 8:74901–74913
Abbas AK, Leonhardt S (2014) Intelligent neonatal monitoring based on a virtual thermal sensor. BMC Med Imaging 14(1):9
Abbas AK, Leonhardt S (2014) Neonatal ir-thermography pattern clustering based on ica algorithm
Avidan S, Shamir A (2007) Seam carving for content-aware image resizing. ACM Trans Graph 26:10. https://doi.org/10.1145/1276377.1276390
Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258
Clark R, Stothers J (1980) Neonatal skin temperature distribution using infra-red colour thermography. J Physiol 302(1):323–333
da Nóbrega RVM, Peixoto SA, da Silva SPP, Rebouċas Filho PP (2018) Lung nodule classification via deep transfer learning in ct lung images. In: 2018 IEEE 31St international symposium on computer-based medical systems (CBMS). IEEE, pp 244–249
Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, Liu Y, Topol E, Dean J, Socher R (2021) Deep learning-enabled medical computer vision. NPJ Digit Med 4(1):1–9
Gour N, Khanna P (2020) Automated glaucoma detection using gist and pyramid histogram of oriented gradients (phog) descriptors. Pattern Recogn Lett 137:3–11
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Hildebrandt C, Zeilberger K, Ring E, Raschner C (2012) The Application of Medical Infrared Thermography in Sports Medicine 10. https://doi.org/10.5772/28383
Huynh BQ, Li H, Giger ML (2016) Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging 3(3):1–5. https://doi.org/10.1117/1.JMI.3.3.034501
Iandola F, Moskewicz M, Karayev S, Girshick R, Darrell T, Keutzer K (2014) Densenet: Implementing efficient convnet descriptor pyramids. arXiv:1404.1869
Kasprzyk-Kucewicz T, Cholewka A, Bałamut K, Kownacki P, Kaszuba N, Kaszuba M, Stanek A, Sieroń K, Stransky J, Pasz A et al (2021) The applications of infrared thermography in surgical removal of retained teeth effects assessment. J Thermal Anal Calorimetry 144(1):139–144
Khan KA, Shanir P, Khan YU, Farooq O (2020) A hybrid local binary pattern and wavelets based approach for eeg classification for diagnosing epilepsy. Expert Syst Appl 140:112895
Kornblith S, Shlens J, Le QV (2019) Do better imagenet models transfer better?. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2661–2671
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Lee SJ, Tseng CH, Lin GR, Yang Y, Yang P, Muhammad K, Pandey HM (2020) A dimension-reduction based multilayer perception method for supporting the medical decision making. Pattern Recogn Lett 131:15–22
Li W, Huang Q, Srivastava G (2021) Contour feature extraction of medical image based on multi-threshold optimization. Mob Netw Appl 26(1):381–389
Mishra C, Bagyammal T, Parameswaran L (2021) An algorithm design for anomaly detection in thermal images. In: Innovations in electrical and electronic engineering. Springer, pp 633–650
Nur R (2014) Identification of thermal abnormalities by analysis of abdominal infrared thermal images of neonatal patients. Ph.D. thesis, Carleton University
Ornek AH, Ceylan M, Ervural S (2019) Health status detection of neonates using infrared thermography and deep convolutional neural networks. Infrared Phys Technol 103:103044
Raj RJS, Shobana SJ, Pustokhina IV, Pustokhin DA, Gupta D, Shankar K (2020) Optimal feature selection-based medical image classification using deep learning model in internet of medical things. IEEE Access 8:58006–58017
Rice H, Hollingsworth C, Bradsher E, Danko M, Crosby S, Goldberg R, Tanaka D, Dail R (2010) Infrared thermal imaging (thermography) of the abdomen in extremely low birthweight infants. J Surg Radiol:1
Rojas-Domínguez A, Padierna LC, Valadez JMC, Puga-Soberanes HJ, Fraire HJ (2017) Optimal hyper-parameter tuning of svm classifiers with application to medical diagnosis. IEEE Access 6:7164–7176
Rosenblatt F (1957) The perceptron, a perceiving and recognizing automaton. Project Para Cornell Aeronautical Laboratory
Savasci D, Ceylan M (2018) Thermal image analysis for neonatal intensive care units (first evaluation results). In: 2018 26Th signal processing and communications applications conference (SIU). IEEE, pp 1–4
Savasci D, Ornek AH, Ervural S, Ceylan M, Konak M, Soylu H (2019) Classification of unhealthy and healthy neonates in neonatal intensive care units using medical thermography processing and artificial neural network. In: Classification techniques for medical image analysis and computer aided diagnosis. Elsevier, pp 1–29
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Stanford: Imagenet (2020). http://www.image-net.org/
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence
Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C (2018) A survey on deep transfer learning. In: International conference on artificial neural networks. Springer, pp 270–279
Villarroel M, Guazzi A, Jorge J, Davis S, Watkinson P, Green G, Shenvi A, McCormick K, Tarassenko L (2014) Continuous non-contact vital sign monitoring in neonatal intensive care unit. Healthcare Technol Lett 1(3):87–91
Vogado LH, Veras RM, Araujo FH, Silva RR, Aires KR (2018) Leukemia diagnosis in blood slides using transfer learning in cnns and svm for classification. Eng Appl Artif Intell 72:415–422
Zhu W, Zeng N, Wang N et al (2010) Sensitivity, specificity, accuracy, associated confidence interval and roc analysis with practical sas implementations. NESUG proceedings: health care and life sciences. Baltimore, Maryland 19:67
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This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK, project number: 215E019).
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Ornek, A.H., Ceylan, M. Medical thermograms’ classification using deep transfer learning models and methods. Multimed Tools Appl 81, 9367–9384 (2022). https://doi.org/10.1007/s11042-021-11852-6
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DOI: https://doi.org/10.1007/s11042-021-11852-6