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Diagnosis and Prognosis of Ultrasound Fetal Growth Analysis Using Neuro-Fuzzy Based on Genetic Algorithms

Diagnosis and Prognosis of Ultrasound Fetal Growth Analysis Using Neuro-Fuzzy Based on Genetic Algorithms

Parminder Kaur, Prabhpreet Kaur, Gurvinder Singh
Copyright: © 2021 |Pages: 62
ISBN13: 9781799827429|ISBN10: 1799827429|EISBN13: 9781799827436
DOI: 10.4018/978-1-7998-2742-9.ch015
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MLA

Kaur, Parminder, et al. "Diagnosis and Prognosis of Ultrasound Fetal Growth Analysis Using Neuro-Fuzzy Based on Genetic Algorithms." Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, edited by Geeta Rani and Pradeep Kumar Tiwari, IGI Global, 2021, pp. 281-342. https://doi.org/10.4018/978-1-7998-2742-9.ch015

APA

Kaur, P., Kaur, P., & Singh, G. (2021). Diagnosis and Prognosis of Ultrasound Fetal Growth Analysis Using Neuro-Fuzzy Based on Genetic Algorithms. In G. Rani & P. Tiwari (Eds.), Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning (pp. 281-342). IGI Global. https://doi.org/10.4018/978-1-7998-2742-9.ch015

Chicago

Kaur, Parminder, Prabhpreet Kaur, and Gurvinder Singh. "Diagnosis and Prognosis of Ultrasound Fetal Growth Analysis Using Neuro-Fuzzy Based on Genetic Algorithms." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, edited by Geeta Rani and Pradeep Kumar Tiwari, 281-342. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-2742-9.ch015

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Abstract

Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. Based upon the analysis of existing algorithms for the automatic fetal development measurement, a new algorithm known as neuro-fuzzy based on genetic algorithm is developed. Firstly, the fetal ultrasound benchmark image is auto-pre-processed using normal shrink homomorphic technique. Secondly, the features are extracted using gray level co-occurrence matrix (GLCM), grey level run length matrix (GLRLM), intensity histogram (IH), and rotation invariant moments (IM). Thirdly, neuro-fuzzy using genetic approach is used to distinguish among the fetus growth as abnormal or normal. Experimental results using benchmark and live dataset demonstrate that the developed method achieves an accuracy of 97% as compared to the state-of-the-art methods in terms of parameters such as sensitivity, specificity, recall, f-measure, and precision rate.

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