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Article

Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data

by 1,2,3, 3, 4,*, 3, 1,2,5 and 6,7,8,9
1
Lambe Institute for Translational Research, National University of Ireland Galway, H91TK33 Galway, Ireland
2
College of Engineering and Informatics, National University of Ireland Galway, H91TK33 Galway, Ireland
3
Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan
4
Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
5
Cardiovascular Research and Innovation Centre Ireland, School of Medicine, National University of Ireland Galway, H91TK33 Galway, Ireland
6
Faculty of Engineering, Université de Moncton, Moncton, NB E1A3E9, Canada
7
International Institute of Technology and Management, Commune d’Akanda, Libreville 1989, Gabon
8
Spectrum of Knowledge Production and Skills Development, Sfax 3027, Tunisia
9
Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa
*
Author to whom correspondence should be addressed.
Academic Editor: Pal Varga
Sensors 2022, 22(14), 5103; https://doi.org/10.3390/s22145103
Received: 13 April 2022 / Revised: 25 June 2022 / Accepted: 4 July 2022 / Published: 7 July 2022
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
Artificial intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient’s outcome. Previous studies only consider classification accuracies of cardiotocographic (CTG) datasets and disregard computational time, which is a relevant parameter in a clinical environment. This paper proposes a modified deep neural algorithm to classify untapped pathological and suspicious CTG recordings with the desired time complexity. In our newly developed classification algorithm, AlexNet architecture is merged with support vector machines (SVMs) at the fully connected layers to reduce time complexity. We used an open-source UCI (Machine Learning Repository) dataset of cardiotocographic (CTG) recordings. We divided 2126 CTG recordings into 3 classes (Normal, Pathological, and Suspected), including 23 attributes that were dynamically programmed and fed to our algorithm. We employed a deep transfer learning (TL) mechanism to transfer prelearned features to our model. To reduce time complexity, we implemented a strategy wherein layers in the convolutional base were partially trained to leave others in the frozen states. We used an ADAM optimizer for the optimization of hyperparameters. The presented algorithm also outperforms the leading architectures (RCNNs, ResNet, DenseNet, and GoogleNet) with respect to real-time accuracies, sensitivities, and specificities of 99.72%, 96.67%, and 99.6%, respectively, making it a viable candidate for clinical settings after real-time validation. View Full-Text
Keywords: fetus classification; deep neural networks; transfer learning; cardiotocography; artificial intelligence; clinical settings fetus classification; deep neural networks; transfer learning; cardiotocography; artificial intelligence; clinical settings
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MDPI and ACS Style

Muhammad Hussain, N.; Rehman, A.U.; Othman, M.T.B.; Zafar, J.; Zafar, H.; Hamam, H. Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data. Sensors 2022, 22, 5103. https://doi.org/10.3390/s22145103

AMA Style

Muhammad Hussain N, Rehman AU, Othman MTB, Zafar J, Zafar H, Hamam H. Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data. Sensors. 2022; 22(14):5103. https://doi.org/10.3390/s22145103

Chicago/Turabian Style

Muhammad Hussain, Nadia, Ateeq Ur Rehman, Mohamed Tahar Ben Othman, Junaid Zafar, Haroon Zafar, and Habib Hamam. 2022. "Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data" Sensors 22, no. 14: 5103. https://doi.org/10.3390/s22145103

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