Improving thyroid cancer diagnosis with advanced deep learning techniques

Abstract

The accurate diagnosis of thyroid carcinoma is a complex and critical challenge in medical practice due to the clinical and pathological diversity of thyroid tumors. This study explores the application of advanced deep learning models, specifically EfficientNet B4 and MobileNetV3, to enhance the classification of thyroid histopathological images. By leveraging transfer learning, these models were fine-tuned on a dataset of 7,272 images encompassing three types of carcinoma: Medullary, Papillary, and Vesicular. The performance of these models was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results indicate that both models are effective, with EfficientNet B4 demonstrating slightly superior accuracy, particularly in more challenging diagnostic scenarios. Despite these promising results, the study acknowledges limitations related to dataset size and variability, which could affect the generalizability of the models. To address these issues, future research should focus on expanding the dataset and incorporating additional diagnostic data, such as genetic and molecular markers, to improve model performance and clinical applicability. This study highlights the potential of deep learning models to significantly enhance the diagnostic accuracy of thyroid carcinomas, paving the way for more reliable and precise clinical decision-making.