RESUMO
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.
