Background The SARS-CoV-2 pandemic began in early 2020, paralyzing human life all over the world and threatening our security. Thus, the need for an effective, novel approach to diagnosing, preventing, and treating COVID-19 infections became paramount. Methods This article proposes a machine learning-based method for the classification of chest X-ray images. We also examined some of the pre-processing methods such as thresholding, blurring, and histogram equalization. Results We found the F1-score results rose to 97%, 96%, and 99% for the three analyzed classes: healthy, COVID-19, and pneumonia, respectively. Conclusion Our research provides proof that machine learning can be used to support medics in chest X-ray classification and improving pre-processing leads to improvements in accuracy, precision, recall, and F1-scores.

In this paper, the analysis of the possibilities of using Digital Image Correlation (DIC) based on Graphics Processing Unit (GPU) for strain analysis in fatigue cracking processes is presented. The basic assumption for the discussed displacement and strain measurement method under time variable loads was obtaining high measurement sensitivity by simultaneously minimizing the measurement time consumption. For this purpose special computing procedures based on multiprocessor graphic cards were developed, which significantly reduced the total time of displacement and strain analysis. The developed digital procedure for correlation of images has been used for an example of displacement analysis in the method of fatigue crack propagation testing in airplane riveted joints. In this paper are presented the results of the researches of the team run by professor Antoni Zabłudowski

Acute lymphoblastic leukemia is the most common cancer in children, and its diagnosis mainly includes microscopic blood tests of the bone marrow. Therefore, there is a need for a correct classification of white blood cells. The approach developed in this article is based on an optimized and small IoT-friendly neural network architecture. The application of learning transfer in hybrid artificial intelligence systems is offered. The hybrid system consisted of a MobileNet v2 encoder pre-trained on the ImageNet dataset and machine learning algorithms performing the role of the head. These were the XGBoost, Random Forest, and Decision Tree algorithms. In this work, the average accuracy was over 90%, reaching 97.4%. This work proves that using hybrid artificial intelligence systems for tasks with a low computational complexity of the processing units demonstrates a high classification accuracy. The methods used in this study, confirmed by the promising results, can be an effective tool in diagnosing other blood diseases, facilitating the work of a network of medical institutions to carry out the correct treatment schedule.