Efficient Deep Learning Architecture for Detection and Recognition of Thyroid Nodules

作者：Jingzhe Ma, Shaobo Duan, Ye Zhang, Jing Wang, Zongmin Wang, Runzhi Li, Yongli Li, Lianzhong Zhang, Huimin Ma · 发表于：Computational Intelligence and Neuroscience · 年份：2020 · DOI：10.1155/2020/1242781 · 被引用次数：36 · 研究领域：Thyroid Cancer Diagnosis and Treatment、AI in cancer detection、Radiomics and Machine Learning in Medical Imaging

Ultrasonography is widely used in the clinical diagnosis of thyroid nodules. Ultrasound images of thyroid nodules have different appearances, interior features, and blurred borders that are difficult for a physician to diagnose into malignant or benign types merely through visual recognition. The development of artificial intelligence, especially deep learning, has led to great advances in the field of medical image diagnosis. However, there are some challenges to achieve precision and efficiency in the recognition of thyroid nodules. In this work, we propose a deep learning architecture, you only look once v3 dense multireceptive fields convolutional neural network (YOLOv3-DMRF), based on YOLOv3. It comprises a DMRF-CNN and multiscale detection layers. In DMRF-CNN, we integrate dilated convolution with different dilation rates to continue passing the edge and the texture features to deeper layers. Two different scale detection layers are deployed to recognize the different sizes of the thyroid nodules. We used two datasets to train and evaluate the YOLOv3-DMRF during the experiments. One dataset includes 699 original ultrasound images of thyroid nodules collected from a local health physical center. We obtained 10,485 images after data augmentation. Another dataset is an open-access dataset that includes ultrasound images of 111 malignant and 41 benign thyroid nodules. Average precision (AP) and mean average precision (mAP) are used as the metrics for quantitative and qualit...