Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection
Abstract Ultrasound (US) imaging is an essential diagnostic technique in prenatal care, enabling enhanced surveillance of fetal growth and development. Fetal ultrasonography standard planes are crucial for evaluating fetal development parameters and detecting abnormalities. Real-time imaging, low co...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-024-01453-8 |
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| author | S. Rathika K. Mahendran H. Sudarsan S. Vijay Ananth |
| author_facet | S. Rathika K. Mahendran H. Sudarsan S. Vijay Ananth |
| author_sort | S. Rathika |
| collection | DOAJ |
| description | Abstract Ultrasound (US) imaging is an essential diagnostic technique in prenatal care, enabling enhanced surveillance of fetal growth and development. Fetal ultrasonography standard planes are crucial for evaluating fetal development parameters and detecting abnormalities. Real-time imaging, low cost, non-invasiveness, and accessibility make US imaging indispensable in clinical practice. However, acquiring fetal US planes with correct fetal anatomical features is a difficult and time-consuming task, even for experienced sonographers. Medical imaging using AI shows promise for addressing current challenges. In response to this challenge, a Deep Learning (DL)-based automated categorization method for maternal fetal US planes are introduced to enhance detection efficiency and diagnosis accuracy. This paper presents a hybrid optimization technique for feature selection and introduces a novel Radial Basis Function Neural Network (RBFNN) for reliable maternal fetal US plane classification. A large dataset of maternal–fetal screening US images was collected from publicly available sources and categorized into six groups: the four fetal anatomical planes, the mother's cervix, and an additional category. Feature extraction is performed using Gray-Level Co-occurrence Matrix (GLCM), and optimization methods such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and a hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO) approach are utilized to select the most relevant features. The optimized features from each algorithm are then input into both conventional and proposed DL models. Experimental results indicate that the proposed approach surpasses conventional DL models in performance. Furthermore, the proposed model is evaluated against previously published models, showcasing its superior classification accuracy. In conclusion, our proposed approach provides a solid foundation for automating the classification of fetal US planes, leveraging optimization and DL techniques to enhance prenatal diagnosis and care. |
| format | Article |
| id | doaj-art-e1f7edb6de444ee48b337e153b272e55 |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-e1f7edb6de444ee48b337e153b272e552024-12-22T12:55:37ZengBMCBMC Medical Imaging1471-23422024-12-0124111910.1186/s12880-024-01453-8Novel neural network classification of maternal fetal ultrasound planes through optimized feature selectionS. Rathika0K. Mahendran1H. Sudarsan2S. Vijay Ananth3Prince Shri Venkateshwara Padmavathy Engineering CollegeSaveetha Engineering CollegeK. Ramakrishnan College of EngineeringChennai Institute of TechnologyAbstract Ultrasound (US) imaging is an essential diagnostic technique in prenatal care, enabling enhanced surveillance of fetal growth and development. Fetal ultrasonography standard planes are crucial for evaluating fetal development parameters and detecting abnormalities. Real-time imaging, low cost, non-invasiveness, and accessibility make US imaging indispensable in clinical practice. However, acquiring fetal US planes with correct fetal anatomical features is a difficult and time-consuming task, even for experienced sonographers. Medical imaging using AI shows promise for addressing current challenges. In response to this challenge, a Deep Learning (DL)-based automated categorization method for maternal fetal US planes are introduced to enhance detection efficiency and diagnosis accuracy. This paper presents a hybrid optimization technique for feature selection and introduces a novel Radial Basis Function Neural Network (RBFNN) for reliable maternal fetal US plane classification. A large dataset of maternal–fetal screening US images was collected from publicly available sources and categorized into six groups: the four fetal anatomical planes, the mother's cervix, and an additional category. Feature extraction is performed using Gray-Level Co-occurrence Matrix (GLCM), and optimization methods such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and a hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO) approach are utilized to select the most relevant features. The optimized features from each algorithm are then input into both conventional and proposed DL models. Experimental results indicate that the proposed approach surpasses conventional DL models in performance. Furthermore, the proposed model is evaluated against previously published models, showcasing its superior classification accuracy. In conclusion, our proposed approach provides a solid foundation for automating the classification of fetal US planes, leveraging optimization and DL techniques to enhance prenatal diagnosis and care.https://doi.org/10.1186/s12880-024-01453-8Ultrasound ImagesFetal organsFast Radial Basis Function Neural NetworkOptimizationFeaturesAccuracy |
| spellingShingle | S. Rathika K. Mahendran H. Sudarsan S. Vijay Ananth Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection BMC Medical Imaging Ultrasound Images Fetal organs Fast Radial Basis Function Neural Network Optimization Features Accuracy |
| title | Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection |
| title_full | Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection |
| title_fullStr | Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection |
| title_full_unstemmed | Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection |
| title_short | Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection |
| title_sort | novel neural network classification of maternal fetal ultrasound planes through optimized feature selection |
| topic | Ultrasound Images Fetal organs Fast Radial Basis Function Neural Network Optimization Features Accuracy |
| url | https://doi.org/10.1186/s12880-024-01453-8 |
| work_keys_str_mv | AT srathika novelneuralnetworkclassificationofmaternalfetalultrasoundplanesthroughoptimizedfeatureselection AT kmahendran novelneuralnetworkclassificationofmaternalfetalultrasoundplanesthroughoptimizedfeatureselection AT hsudarsan novelneuralnetworkclassificationofmaternalfetalultrasoundplanesthroughoptimizedfeatureselection AT svijayananth novelneuralnetworkclassificationofmaternalfetalultrasoundplanesthroughoptimizedfeatureselection |