Parveen H Summia, Karthik S, M S Kavitha
Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore-641202.
Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, India.
Comput Methods Biomech Biomed Engin. 2024 Apr 22:1-18. doi: 10.1080/10255842.2024.2341969.
In the contemporary world, thyroid disease poses a prevalent health issue, particularly affecting women's well-being. Recognizing the significance of maternal thyroid (MT) hormones in fetal neurodevelopment during the first half of pregnancy, this study introduces the HNN-GSO model. This groundbreaking hybrid approach, utilizing the MT dataset, integrates ResNet-50 and Artificial Neural Network (ANN) within a Glow-worm Swarm Optimization (GSO) framework for optimal parameter tuning. With a comprehensive methodology involving dataset preprocessing and Genetic Algorithm (GA) for feature selection, our model leverages ResNet-50 for feature extraction and ANN for classification tasks. Implemented in Python, the HNN-GSO model outperforms existing models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), ResNet, GoogleNet, and ANN, achieving an impressive accuracy rate of 98%. This success underscores the effectiveness of our approach in complex classification tasks within machine learning (ML) and pattern recognition, specifically tailored to the Thyroid Ultrasound Images (TUI) Dataset. To provide a comprehensive understanding of performance, additional statistical measures such as precision, recall, and F1 score were considered. The HNN-GSO model consistently outperformed competitors across these metrics, showcasing its superiority in MT classification. The HNN-GSO model seamlessly combines ResNet-50's feature extraction, ANN's classification robustness, and GSO's optimization for unparalleled performance. This research offers a promising framework for advancing ML methodologies, enhancing accuracy, and efficiency in classification tasks related to MT health.
在当代世界，甲状腺疾病是一个普遍存在的健康问题，尤其影响女性的健康。认识到孕期前半期母体甲状腺（MT）激素在胎儿神经发育中的重要性，本研究引入了HNN-GSO模型。这种开创性的混合方法利用MT数据集，在萤火虫群优化（GSO）框架内集成了ResNet-50和人工神经网络（ANN），以实现最佳参数调整。通过涉及数据集预处理和遗传算法（GA）进行特征选择的综合方法，我们的模型利用ResNet-50进行特征提取，利用ANN进行分类任务。HNN-GSO模型用Python实现，优于现有模型，包括K近邻（KNN）、支持向量机（SVM）、逻辑回归（LR）、随机森林（RF）、ResNet、GoogleNet和ANN，达到了令人印象深刻的98%的准确率。这一成功凸显了我们的方法在机器学习（ML）和模式识别中的复杂分类任务中的有效性，特别是针对甲状腺超声图像（TUI）数据集量身定制的任务。为了全面了解性能，还考虑了其他统计指标，如精确率、召回率和F1分数。HNN-GSO模型在这些指标上始终优于竞争对手，展示了其在MT分类中的优越性。HNN-GSO模型无缝结合了ResNet-50的特征提取、ANN的分类稳健性以及GSO的优化，实现了无与伦比的性能。这项研究为推进ML方法、提高与MT健康相关的分类任务的准确性和效率提供了一个有前景的框架。
