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Artificial Intelligence and Image Analysis for the Identification of Endometrial Malignancies: A Comparative Study

Artificial Intelligence and Image Analysis for the Identification of Endometrial Malignancies: A Comparative Study

Abraham Pouliakis, Vasileia Damaskou, Niki Margari, Efrossyni Karakitsou, Vasilios Pergialiotis, George Valasoulis, George Michail, Charalampos Chrelias, George Chrelias, Vasileios Sioulas, Alina-Roxani Gouloumi, Nektarios Koufopoulos, Martha Nifora, Andriani Zacharatou, Sophia Kalantaridou, Ioannis G. Panayiotides
Copyright: © 2020 |Pages: 37
ISBN13: 9781799823902|ISBN10: 1799823903|EISBN13: 9781799823919
DOI: 10.4018/978-1-7998-2390-2.ch005
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MLA

Pouliakis, Abraham, et al. "Artificial Intelligence and Image Analysis for the Identification of Endometrial Malignancies: A Comparative Study." Quality Assurance in the Era of Individualized Medicine, edited by Anastasius S. Moumtzoglou, IGI Global, 2020, pp. 110-146. https://doi.org/10.4018/978-1-7998-2390-2.ch005

APA

Pouliakis, A., Damaskou, V., Margari, N., Karakitsou, E., Pergialiotis, V., Valasoulis, G., Michail, G., Chrelias, C., Chrelias, G., Sioulas, V., Gouloumi, A., Koufopoulos, N., Nifora, M., Zacharatou, A., Kalantaridou, S., & Panayiotides, I. G. (2020). Artificial Intelligence and Image Analysis for the Identification of Endometrial Malignancies: A Comparative Study. In A. Moumtzoglou (Ed.), Quality Assurance in the Era of Individualized Medicine (pp. 110-146). IGI Global. https://doi.org/10.4018/978-1-7998-2390-2.ch005

Chicago

Pouliakis, Abraham, et al. "Artificial Intelligence and Image Analysis for the Identification of Endometrial Malignancies: A Comparative Study." In Quality Assurance in the Era of Individualized Medicine, edited by Anastasius S. Moumtzoglou, 110-146. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2390-2.ch005

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Abstract

The aim of this study is to compare machine learning algorithms (MLAs) in the discrimination between benign and malignant endometrial nuclei and lesions. Nuclei characteristics are obtained via image analysis and were measured from liquid-based cytology slides. Four hundred sixteen histologically confirmed patients were involved, 168 healthy, and the remaining with pathological endometrium. Fifty percent of the cases were used to three MLAs: a feedforward artificial neural network (ANN) trained by the backpropagation algorithm, a learning vector quantization (LVQ), and a competitive learning ANN. The outcome of this process was the classification of cell nuclei as benign or malignant. Based on the nuclei classification, an algorithm to classify individual patients was constructed. The sensitivity of the MLAs in training set for nuclei classification was in the range of 77%-84%. Patients' classification had sensitivity in the range of 90%-98%. These findings indicate that MLAs have good performance for the classification of endometrial nuclei and lesions.

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