Deep learning-assisted diagnosis of benign and malignant parotid tumors based on ultrasound: a retrospective study.

BACKGROUND: To develop a deep learning(DL) model utilizing ultrasound images, and evaluate its efficacy in distinguishing between benign and malignant parotid tumors (PTs), as well as its practicality in assisting clinicians with accurate diagnosis. METHODS: A total of 2211 ultrasound images of 980 pathologically confirmed PTs (Training set: n = 721; Validation set: n = 82; Internal-test set: n = 89; External-test set: n = 88) from 907 patients were retrospectively included in this study. The optimal model was selected and the diagnostic performance evaluation is conducted by utilizing the area under curve (AUC) of the receiver-operating characteristic(ROC) based on five different DL networks constructed at varying depths. Furthermore, a comparison of different seniority radiologists was made in the presence of the optimal auxiliary diagnosis model. Additionally, the diagnostic confusion matrix of the optimal model was calculated, and an analysis and summary of misjudged cases' characteristics were conducted. RESULTS: The Resnet18 demonstrated superior diagnostic performance, with an AUC value of 0.947, accuracy of 88.5%, sensitivity of 78.2%, and specificity of 92.7% in internal-test set, and with an AUC value of 0.925, accuracy of 89.8%, sensitivity of 83.3%, and specificity of 90.6% in external-test set. The PTs were subjectively assessed twice by six radiologists, both with and without the assisted of the model. With the assisted of the model, both junior and senior radiologists demonstrated enhanced diagnostic performance. In the internal-test set, there was an increase in AUC values by 0.062 and 0.082 for junior radiologists respectively, while senior radiologists experienced an improvement of 0.066 and 0.106 in their respective AUC values. CONCLUSIONS: The DL model based on ultrasound images demonstrates exceptional capability in distinguishing between benign and malignant PTs, thereby assisting radiologists of varying expertise levels to achieve heightened diagnostic performance, and serve as a noninvasive imaging adjunct diagnostic method for clinical purposes.

An improved faster R-CNN algorithm for assisted detection of lung nodules.

The morbidity and mortality of lung cancer are increasing rapidly in every country in the world, and pulmonary nodules are the main symptoms of lung cancer in the early stage. If we can diagnose pulmonary nodules in time at the early stage and follow up and treat suspicious patients, we can effectively reduce the incidence of lung cancer. CT (Computed Tomography) has been applied to the screening of many diseases because of its high resolution. Pulmonary nodules show white round shadows in CT images. With the popularity of CT equipment, doctors need to review a large number of imaging results every day. Doctors will misjudge and miss the lesions because of reviewing CT scanning results for a long time. At this time, the method of automatic detection of pulmonary nodules by computer can relieve the pressure of doctors in reviewing CT scan results. Traditional lung nodule detection methods, such as gray threshold method and region growing method, divide the detection process into two steps: extracting candidate regions and eliminating false regions. In addition, the traditional detection method can only operate on a single image, which leads to the inability of this method to detect the batch scanning results in real time. With the continuous development of computer equipment performance and artificial intelligence, the relationship between medical image processing and deep learning is getting closer and closer. In deep learning, object detection methods such as Faster R-CNN、YOLO can complete parallel detection of batch images, and deep structure can fully extract the features of input images. Compared with traditional lung nodule detection methods, it has the characteristics of high efficiency and high precision. Faster R-CNN is a classical and high-precision two-stage object detection method. In this paper, an improved Faster R-CNN model is proposed. On the basis of Faster R-CNN, multi-scale training strategy is used to fully mine the features of different scale spaces and perform path augmentation on lower-dimensional features, which improves the small object detection ability of the model. Through Online Hard Example Mining (OHEM), the loss value is used to quantify the difficulty of candidate region detection, and the training times of the region to be detected are adaptively adjusted. Make full use of prior information to customize the size and proportion of preset boundary anchor boxes. Using deformable convolution to improve the visual field to enhance the global features and enhance the ability to extract the feature information of pulmonary nodules in the same scale space. The new model was tested on LUNA16 (Lung Nodule Analysis 2016) dataset. The detection precision of the improved Faster R-CNN model for pulmonary nodules increased from 76.4% to 90.7%, and the recall rate increased from 40.1% to 56.8% Compared with the mainstream object detection algorithms YOLOv3 and Cascade R-CNN, the improved model is superior to the above models in every index.