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Mục tiêu: Đánh giá ảnh hưởng một số yếu tố nguy cơ đến kết quả phẫu thuật sớm điều trị thoát vị bẹn người cao tuổi theo phương pháp Lichtenstein tại Bệnh viện Hữu Nghị Việt Tiệp. Đối tượng và phương pháp nghiên cứu: Nghiên cứu mô tả tiến cứu. Gồm 40 bệnh nhân trên 60 tuổi được phẫu thuật điều trị thoát vị bẹn bằng phương pháp Lichtenstein tại bệnh viện Hữu nghị Việt Tiệp từ tháng 06/2020 đến hết tháng 06/2022. Kết quả: Tỷ lệ biến chứng chung sớm sau mổ là 15%. Thời gian đau sau mổ trung bình 5,23 ± 1,27 ngày, thời gian nằm viện sau mổ trung bình 7,95 ± 2,05 ngày. Phần lớn bệnh nhân phục hồi sinh hoạt cá nhân sau mổ trong vòng 24 giờ. Các yếu tố liên quan đến kết quả phẫu thuật thoát vị bẹn ở người cao tuổi bao gồm: tuổi cao, bệnh lý làm tăng áp lực ổ bụng thường xuyên và chỉ số BMI. Đánh giá kết quả phẫu thuật sớm cho thấy tỷ lệ bệnh nhân có kết quả tốt chiếm 85%, khá 5% và trung bình chiếm 10%.

Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required. In this paper, we introduce a robust deep learning based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23%.

Access to the floor plans of historical monuments over a time period is necessary in order to understand the architectural evolution and history. Such knowledge also helps to review (rebuild) the history by establishing connections between different events, persons and facts which were once part of the buildings. Since the two-dimensional plans do not capture the entire space, 3D modeling sheds new light on these unique archives and thus opens up great perspectives for understanding the ancient states of the monument. The first step towards generating the 3D model of the buildings and/or monuments is the wall detection inside the floor plan. Henceforth, the current work introduces a novel Versailles-FP dataset consisting Versailles Palace floor plan images and groundtruth in the form of wall masks regarding architectural developments during \(17^{th}\) and \(18^{th}\) century. The wall masks of the dataset are generated using an automated multi-directional steerable filters approach. The generated wall masks are then validated and corrected manually. We validate our approach of wall-mask generation in state-of-the-art modern datasets. Finally we propose a U-net based convolutional framework for wall detection. We have empirically shown that our U-net based method architecture achieves state-of-the-art results surpassing fully connected network based approach.

Biometric recognition systems provide an easy way to verify an individual’s identity through physiological and behavioral biometric traits, due to the persistence of these traits. The physiological traits are extensively utilized to secure the applications of numerous fields. Among these, toe print is one of the physiological traits that has been discussed and evaluated for the children; however, it has not been addressed in the past as this can also be utilized to verify the identity of an adult, especially for a person with different abilities. In this paper, we have come up with a feasibility study of toe prints while comparing them with the impression of a person’s different fingers in terms of the overall performance of a biometric verification system. To accomplish this task, the toe print database is collected from different persons of age varying from 17 years to 64 years, which is the first database of its kind and is made available in public domain. Fingerprint impressions are also collected along with the toe print to compare the reliability of the toe print with respect to performance using a standard fingerprint verification tool. Verifinger trial version has been utilized by considering the two standard fingerprint verification protocols viz. 1-vs-1 and FVC to assess the performance of the toe print verification system in terms of the equal error rate (EER). The toe print verification system attains 0.04% and 0.01% EER values for 1-vs-1 and FVC protocol, respectively, which clearly depicts the feasibility of toe print as a potential biometric trait.