| dc.description.abstract | The thyroid tumor is a common condition in the endocrine system. Thyroid tumors typically pose minimal risk and do not significantly impact daily life. However, when a thyroid tumors is diagnosed as a malignant tumor or when the tumor is excessively large and compressing the trachea, immediate surgical treatment is necessary. Thyroid tumors are typically detected using ultrasound, a simple, quick, cost-effective, non-invasive, and radiation-free method of examination. However, for a more detailed assessment of the benign or malignant nature of thyroid nodules, fine-needle aspiration (FNA) is required to extract cells from the tumor for microscopic examination to determine the type of tumor. Currently, the interpretation of thyroid nodules in ultrasound images still relies on the visual assessment of clinical physicians. This method is not only time-consuming but also susceptible to interpretation variations based on experience, affecting the accuracy of diagnosis.
This study proposes the development of an automatic thyroid nodule detection system based on the YOLO v4 deep neural network. This system aims to achieve rapid detection of thyroid nodules in ultrasound images. We selected seven pre-trained convolutional neural networks as feature extraction networks and combined them with the YOLO v4 network, creating seven customized YOLO v4 detectors. We utilized image inpainting techniques to remove artifacts from the ultrasound images and employed data augmentation to increase the image quantity and enhance image contrast. After 5-fold cross-validation, when the feature extraction network was NASNet-Large, the model achieved the following evaluation metrics on the test set: average Precision = 92.2%, average Recall = 85.7%, average F1-score = 88.8%, and average AP = 84%. We concluded by designing a graphical user interface to assist clinical physicians in efficiently diagnosing thyroid nodules in ultrasound images, providing them with convenience and speed during the diagnostic process. | en_US |