Deep learning and Gaussian Mixture Modelling clustering mix. A new approach for fetal morphology view plane differentiation

• Published in: Journal of biomedical informatics Vol. 143; p. 104402
• Main Authors: Belciug, Smaranda, Iliescu, Dominic Gabriel
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2023
• Subjects: Deep learning; Fetal morphology; Performance analysis; Statistical assessment; Weighted voting; Deep learning; Weighted voting; Performance analysis; Fetal morphology; Statistical assessment
• ISSN: 1532-0464; 1532-0480; 1532-0480
• DOI: 10.1016/j.jbi.2023.104402

[Display omitted] The last three years have been a game changer in the way medicine is practiced. The COVID-19 pandemic changed the obstetrics and gynecology scenery. Pregnancy complications, and even death, are preventable due to maternal-fetal monitoring. A fast and accurate diagnosis can be established by a doctor + Artificial Intelligence combo. The aim of this paper is to propose a framework designed as a merger between Deep learning algorithms and Gaussian Mixture Modelling clustering applied in differentiating between the view planes of a second trimester fetal morphology scan. The deep learning methods chosen for this approach were ResNet50, DenseNet121, InceptionV3, EfficientNetV2S, MobileNetV3Large, and Xception. The framework establishes a hierarchy of the component networks using a statistical fitness function and the Gaussian Mixture Modelling clustering method, followed by a synergetic weighted vote of the algorithms that gives the final decision. We have tested the framework on two second trimester morphology scan datasets. A thorough statistical benchmarking process has been provided to validate our results. The experimental results showed that the synergetic vote of the framework outperforms the vote of each stand-alone deep learning network, hard voting, soft voting, and bagging strategy.