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Image processing is widely used in the medical field, which helps medical teams diagnose diseases patients suffer. Technology used to diagnose conditions such as X-rays, CT scans, and MRI. One of the diseases that use X-ray technology to analyze is tuberculosis. Tuberculosis (TB) is an infectious disease that causes death worldwide. The Mycobacteria tuberculosis bacillus causes TB. This disease spreads when tuberculosis sufferers release bacteria into the air. Pediatric pulmonary TB has experienced increased mortality rates and has become a challenge in diagnosing and treating it. This study uses edge detection method to analyze X-ray images of pulmonary TB in children, namely conventional edge detection(sobel operator, canny, Prewitt, Robert, zero crossing, LoG) and the development of edge detection named MEMG (Modification and Extended Magnitude Gradient). This research aims to analyze performance comparisons of operators Sobel, canny, Prewitt, Robert, zero crossing, LoG, and MEMG. The analysis to determine quality image visualization of tuberculosis used parameter variables MSE, RMSE, and PSNR for the best results among several edge detection methods. According to the calculation result by MatLab, the best edge detection is obtained with the average values of MSE at 0,37418, the RMSE at 0,6105925, and the PSNR obtained at 52,43185 dB. The importance of MSE and RMSE in the MEMG scheme is lower than the six existing edge detections, providing higher PSNR values and exceeding the standard of 30 dB. [ABSTRACT FROM AUTHOR]

Novel Corona-virus (COVID-19) must be recognized immediately and precisely to avoid or contain a possible pandemic by immediate quarantine and appropriate medical treatment. Detecting a disease will be challenging due to the increased number of COVID-19 patients and the virus' mutation. However, computer-assisted medical diagnosis has attained cutting-edge outcomes using artificial intelligence and deep learning methodologies. This study uses current resources and powerful deep learning methods to present an alternative diagnostic tool for COVID-19 instances. This work aims to investigate the feasibility of combining state-of-the-art classifiers for the most accurate detection of COVID-19 from chest X-ray images. The model utilizes the IEEE covid chest X-ray dataset (https://github.com/ieee8023/covid-chestxray-dataset) and the EfficientNet architecture to predict an accuracy of 99 percent. As a result, a model such as this one can assist medical practitioners in diagnosing COVID-19 cases more quickly than a radiologist reading through each scan one by one, mainly when many patients must be evaluated in short period. [ABSTRACT FROM AUTHOR]

The detection of chest diseases from X-ray images is a crucial aspect of medical diagnostics, playing a vital role in the early identification and treatment of respiratory conditions. This review paper gives a general overview of how machine learning algorithms are used to identify chest diseases from X-ray pictures. It emphasizes the importance of accurate and timely diagnosis of chest diseases, while acknowledging the challenges faced by conventional diagnostic methods. The review highlights the potential of machine learning as a promising approach to enhance diagnostic accuracy and efficiency in this field. It explores the utilization of both handcrafted features and deep learning-based approaches for extracting informative features from chest X-rays. Furthermore, it discusses the adoption of various machine learning algorithms, including support vector machines, random forests, and convolutional neural networks, for effective detection of chest diseases. The review paper also recognizes the significance of training and validation strategies in ensuring the robust development of models. Additionally, it addresses the potential impact of the proposed methodology on chest disease detection and patient outcomes. The review paper discusses challenges and limitations associated with this approach, such as data availability, interpretability, and ethical considerations. In summary, this extensive analysis strives to make a valuable contribution to the field by offering insights into cutting-edge machine learning techniques, recognizing existing challenges, and proposing potential avenues for future research. [ABSTRACT FROM AUTHOR]

The paper considers the problem of using end-to-end digital technologies and platform solutions using elements of artificial intelligence. Identification of goods prohibited to be transported across the customs border is accepted as the main object of research. Problematic issues of unambiguous identification of prohibited goods at the border with the use of inspection and inspection complexes are formulated. The dual energy method for recognizing the materials of a controlled object by the effective atomic number and the mathematical foundations of a computed tomography are outlined. Recommendations on the implementation of the method of nonlinear inverse projection and nonlinear tomosynthesis at a small number of projection angles of the controlled object of the developed model in practice are given. Around the world, the time of customs clearance of foreign trade cargoes is one of the key criteria for assessing the effectiveness of the customs service. The use of end-to-end digital technologies and platform solutions using artificial intelligence elements and decision support systems will make it possible to carry out customs control "invisibly" and quickly throughout the entire chain of goods movement [1]. As a tool to speed up customs procedures, prevent and suppress offenses at the border, high-tech equipment is used, including large-sized inspection complexes (IC). Currently, the customs authorities of the Republic of Uzbekistan are equipped with modern IC, which allow you to get an x-ray image of the vehicle and the goods transported in it. However, the analysis of X-ray images and the detection of items prohibited for transportation in them requires high qualifications from a customs officer. Consequently, the task of studying the methods of artificial intelligence and recognition of goods prohibited for transport across the customs border is relevant. Therefore, the task of researching artificial intelligence methods and automatic recognition of goods prohibited for transportation across the customs border is relevant. [ABSTRACT FROM AUTHOR]

Many medical images processing tasks, including chest radiography, have recently been demonstrated to be significantly improved by AI researchers who have used deep learning, particularly CNN. To help radiologists diagnose thoracic disorders, they are required to assist. Determining the presence of one or two thoracic diseases using deep learning models was the driving force behind an effort to construct a real-time, multi-thoracic disease detection and classification model. In this article, we will review breakthrough applications built with deep learning models such as CNNs to detect and classify multiple pathologies in one exam on Chest radiography. Also, we will discuss important design factors and future trends in computer aided diagnosis of multi-disease classification problems in Chest Radiology. [ABSTRACT FROM AUTHOR]