Abstract Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.

Abstract This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.

Abstract In recent years, the pathological diagnosis of glomerular diseases typically involves the study of glomerular his-to pathology by specialized pathologists, who analyze tissue sections stained with Periodic Acid-Schiff (PAS) to assess tissue and cellular abnormalities. In recent years, the rapid development of generative adversarial networks composed of generators and discriminators has led to further developments in image colorization tasks. In this paper, we present a generative adversarial network by Spectral Normalization colorization designed for color restoration of grayscale images depicting glomerular cell tissue elements. The network consists of two structures: the generator and the discriminator. The generator incorporates a U-shaped decoder and encoder network to extract feature information from input images, extract features from Lab color space images, and predict color distribution. The discriminator network is responsible for optimizing the generated colorized images by comparing them with real stained images. On the Human Biomolecular Atlas Program (HubMAP)—Hacking the Kidney FTU segmentation challenge dataset, we achieved a peak signal-to-noise ratio of 29.802 dB, along with high structural similarity results as other colorization methods. This colorization method offers an approach to add color to grayscale images of glomerular cell tissue units. It facilitates the observation of physiological information in pathological images by doctors and patients, enabling better pathological-assisted diagnosis of certain kidney diseases.

Abstract To save patients’ lives, it is important to go for an early diagnosis of intracranial hemorrhage (ICH). For diagnosing ICH, the widely used method is non-contrast computed tomography (NCCT). It has fast acquisition and availability in medical emergency facilities. To predict hematoma progression and mortality, it is important to estimate the volume of intracranial hemorrhage. Radiologists can manually delineate the ICH region to estimate the hematoma volume. This process takes time and undergoes inter-rater variability. In this research paper, we develop and discuss a fine segmentation model and a coarse model for intracranial hemorrhage segmentations. Basically, two different models are discussed for intracranial hemorrhage segmentation. We trained a 2DDensNet in the first model for coarse segmentation and cascaded the coarse segmentation mask output in the fine segmentation model along with input training samples. A nnUNet model is trained in the second fine stage and will use the segmentation labels of the coarse model with true labels for intracranial hemorrhage segmentation. An optimal performance for intracranial hemorrhage segmentation solution is obtained.

Abstract Problem : There is a need for effective and non-invasive techniques for early cancer detection to improve treatment outcomes and patient care. Motivation : This research explores the potential of thermal imaging as a non-invasive technique for cancer detection. Aim : The aim of this study is to investigate thermal imaging as a valuable tool for early cancer detection and its potential to enhance treatment outcomes and patient care. Methodology : The paper discusses the principles of thermal imaging, its advantages and limitations, and its application to various types of cancer. It also presents a review of recent studies in the field. Main results : The findings suggest that thermal imaging holds promise as a valuable tool for early cancer detection. Further impact of those results : The potential application of thermal imaging in cancer detection could lead to improved treatment outcomes and enhance overall patient care. The article also highlights the challenges and future prospects of thermal imaging in this domain.

Abstract Publications overestimating the medical and ecological sequels of a slight anthropogenic increase in the radiation background have been reviewed recently with examples of different organs and pathological conditions. The overestimation contributed to the strangulation of atomic energy. The use of nuclear energy for electricity production is on the agenda today due to the increasing energy needs of humankind. Apparently, certain scientific writers acted in the interests of fossil fuel producers. Health risks and environmental damage are maximal for coal and oil, lower for natural gas, and much lower for atomic energy. This letter is an addition to previously published materials, this time focused on studies of cataracts in radiation-exposed populations in Russia. Selection and self-selection bias are of particular significance. Apparently, the self-reporting rate correlates with dose estimates and/or with professional awareness about radiation-related risks among nuclear workers or radiologic technologists, the latter being associated with their work experience/duration and hence with the accumulated dose. Individuals informed of their higher doses would more often seek medical advice and receive more attention from medics. As a result, lens opacities are diagnosed in exposed people earlier than in the general population. This explains dose-effect correlations proven for the incidence of cataracts but not for the frequency of cataract surgeries. Along the same lines, various pathological conditions are more often detected in exposed people. Ideological bias and the trimming of statistics have not been unusual in the Russian medical sciences. It is known that ionizing radiation causes cataracts; however, threshold levels associated with risks are understudied. In particular, thresholds for chronic and fractionated exposures are uncertain and may be underestimated.