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Automatic analysis of medical images and endoscopic images, in particular, is an attractive research topic in recent years. To achieve this goal, many tasks must be conducted for example lesion detection, segmentation, and classification. However, existing methods for such problems still face challenges due to the spreading characteristic of the lesions as well as some artifacts caused by motion, specularities, low contrast, bubbles, debris, bodily fluid, and blood. As a consequence, single segmentation or detection could not deal with those issues. Besides, although deep learning has achieved impressive results in many tasks, lacking a large annotated dataset could lead to overfitting problems. In this paper, we tackle these issues by taking particular characteristics of lesions of interest and advantages of deep segmentation models into account. We propose a dual-path framework (namely, DCS-UNet) that combines both segmentation and classification. We first segment lesions from the image using U-Net architecture with different encoder backbones (ResNet-50, VGG-16, DenseNet-201). The segmented regions will be then refined in the second path where we classify every patch in the whole image or the Inner or Outer regions extended from the contours given by the segmentation results. For the refinement scheme, we utilize the color-dependent and texture features as doctors’ advice and deploy the support vector machine (SVM) technique to classify a patch into disease region or not. Extensive experiments were conducted on three datasets of gastroesophageal reflux disease (GERD) endoscopic images as GradeM, GradeA, GradeB, which are defined as modified Los Angeles classification. The experimental results show the improvements of the proposed method compared to the single U-Net or a segmentation using the hand-designed features-based scheme. The proposed method improves mDice and mIoU by 0.5% and 0.36% on GradeA dataset, which is the most challenging dataset with ambiguous separations of GERD vs. normal regions, and by 0.82% and 0.81% on GradeM dataset, which is clearer to segment GERD regions. This proposed framework shows the potential to develop a diagnosis assistance system to help endoscopists reduce burden in examining GERD in the future.

The purpose of the research is to examine the offshore wind power potential in the mix-electric power of Vietnam. By using net present value method, the paper also aims to explore how economic factors influence the offshore wind power feasibility in the conditions that the technical factors are ensured. The result indicates that offshore wind power will have a low proportion in the mix-electric power of Vietnam before 2030 and may be accelerated in the period 2035 – 2040. Electricity supply from offshore wind is not economically viable without improvements in investment, operating costs, and capacity factor. In particular, the Feed-in-Tariff (FIT) policy designed to support the development of renewable energy sources by providing a guaranteed, above-market price for producers, will be a decisive factor in developing offshore wind power of Vietnam. Additionally, the differences between characteristics of site's condition leading to the disparities in capital and operational expenditure and capacity factor. Thus, the electricity price and the subsidies for bottom-fixed sites and floating sites should be disparate and the location of offshore wind farms should be considered to ensure the harmonization of the profits derived from different types of offshore wind farms.