Thyroid Disease Detection through EfficientNetB2: Enhancing Early Diagnosis with Deep Learning

Thyroid diseases—including hyperthyroidism and hypothyroidism—can seriously damage human well-being influencing cognitive ability, heart health, and fertility. Early, correct identification of these disorders guarantees both better patient outcomes and efficient treatment. Among the standard diagnoses are imaging and blood tests; both may be costly and time-consuming. This work suggests applying deep learning to increase diagnostic accuracy and efficiency by means of a modified EfficientNetB2 model for the automated categorization of thyroid disorders. Divided into two groups—healthy (0) and thyroid illness (1)—the model was taught using a collection of 7,288 thyroid-related photos. Pre-processing, picture resizing, standardizing, and enriching the dataset assured model robustity. Precision, recall, and F1-scores of 0.94, 0.88, and 0.91 for class 1 (thyroid illness) and 0.86, 0.93, and 0.90 for class 0 (healthy) the EfficientNet B2 model exhibited an outstanding accuracy of 90% in identifying thyroid abnormalities. By use of a confusion matrix, loss curves, and classification criteria, the model's performance was assessed, thereby exposing a well-balanced strategy to lower false positives and negatives. By means of better access to early detection techniques, encouragement of well-being, and support of the creation of creative medical solutions fit for sustainability objectives, this work promotes healthcare. The results demonstrate the potential of the instrument for early thyroid illness identification since EfficientNetB2 presents a scalable and computationally fast automated medical diagnostics solution.