Colonoscopy is essential for examining colorectal polyp or cancer. Examining colonoscopy has allowed for a reduction in the incidence and mortality of colorectal cancer through the detection and removal of polyps. However, missed polyp rate during colonoscopy has been reported as approximately 24% and intra- and inter-observer variability for polyp detection rates among endoscopists has been an issue. In this paper, we propose a real-time deep learning-based colorectal polyp detection system called SmartEndo-Net. To extract the polyp information, ResNet-50 is used in the backbone. To enable high-level feature fusion, extra mix-up edges in all level of the fusion feature pyramid network (FPN) are added. Fusion features are fed to a class and box network to produce object class and bounding box prediction. SmartEndo-Net is compared with Yolo-V3, SSD, and Faster R-CNN. SmartEndo-Net recorded sensitivity of 92.17% and proposed network was higher 7.96%, 6.78%, and 10.05% than Yolo-V3, SSD, and Faster R-CNN. SmartEndo-Net showed stable detection results regardless of polyp size, shape, and surrounding structures.
Recently, the need for liver transplantation has increased as the number of liver cancer and liver cirrhosis patients increases. The preoperative measurement of the liver volume of the donor is very important. The liver volume is one of analysis factors to predict liver function. However, the current process of liver volume is manually measured by radiologist from CT data, and it takes a lot of time and effort. In this paper, we propose a Deep 3D Attention U-Net for the whole liver segmentation that learns to focus on liver structures of varying shapes and sizes. In addition, the whole liver volume was calculated in voxel units using the segmentation result. The liver segmentation studies of the 266 patients are randomly assigned into train, validation and test sets, with a split ratio of 80%, 10% and 10% of total amount of patients, respectively. The results of liver segmentation achieved sensitivity of 0.914, the specificity of 0.999, and the dice similarity coefficient of 0.936. The relationship analysis of the liver volume showed the correlation coefficient r of 0.853 between manually measured liver volume and calculated liver volume using segmentation result. The results of liver volume measurements through whole liver segmentation based on Deep 3D Attention U-Net were similar to a reliable level.
The purpose of this study is to use a deep learning model to identify the possibility of lesions in the cervix and to evaluate the efficient image preprocessing in order to diagnose diverse types of cervix in form. The study used 4,107 normal photographs of uterine cervix and 6,285 abnormal photographs of uterine cervix. Under the same size condition, to see if which method is more effective to performance either removal of the vaginal wall area or diagnosing cervical cancer including the vaginal wall area, two types of image preprocessing were resized to square. The average accuracy of cropped cases is 94.15%. The average accuracy of the filled cases is 93.41%.
A novel method for simultaneous measurements of the differential effective index and the thermo-optic coefficient ofafiber is presented. The method is based on the interference in a long-period fiber grating pair.
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