Thyroid Health Forecaster: A Cutting-Edge Hybrid Deep Learning Framework for Early Disease Prediction

Detecting thyroid disease early is crucial for effective management and treatment, as conditions like hypothyroidism and hyperthyroidism can significantly impact a patient's health if left untreated. Traditional diagnostic methods, primarily based on hormone level testing, often fail to detect the disease in its initial stages. This study introduces a hybrid deep learning strategy to predict thyroid disease at an earlier stage, utilizing a combination of ResNet50 for feature extraction and Support Vector Machine (SVM) for classification. The methodology involves preprocessing thyroid image data, including resizing, normalization, augmentation, noise reduction, and enhancement, followed by feature extraction and classification. The hybrid model achieved an accuracy of 95.2%, precision of 94.5%, recall of 93.8%, F1 score of 94.1%, and ROC-AUC of 97%. This approach demonstrates superior performance compared to traditional machine learning models, indicating its potential for reliable early diagnosis of thyroid conditions.