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COVID-19 disease classification based on analysis of chest X-rays
Šteflík, Dominik ; Kiac, Martin (referee) ; Myška, Vojtěch (advisor)
This diploma thesis addresses the development and evaluation of artificial intelligence algorithms for classifying COVID-19 disease from chest X-ray images. Given the severity and impact of the COVID-19 pandemic on the global population, the ability to rapidly and accurately diagnose diseases from radiographic images has become critical. This study synthesizes current advancements in image processing and deep learning to evaluate the application of several novel classification methods in practice. Using a dataset obtained from a Czech medical environment, these methods are analyzed and validated in order to examine their effectiveness and accuracy in real life scenarios. The methods chosen for this study, COVID-Net, DarkCovidNet, and CoroNet, were selected due to their availability, widespread use and proven effectiveness in the field. The core of the thesis is the design of a convolutional neural network tailored to extract and learn from the subtle features present in X-ray images indicative of COVID-19. This initiative confronted significant challenges posed by variable acquisition parameters of X-ray images, which can substantially affect diagnostic accuracy. The uniformity of these parameters is crucial for reliable analysis, underscoring the importance of rigorous preprocessing techniques. In response, advanced normalization, contrast adjustment, and augmentation procedures were implemented to standardize the input data. The convolutional network itself employs a series of convolutional, pooling, and fully connected layers, optimized to handle the nuanced variations present in medical imaging data. Notably, the network architecture incorporates an attention mechanism, implemented through a Squeeze-and-Excitation block, to dynamically adjust the importance of different channels in the input image. By integrating these elements, the network model is trained to focus on significant features within the X-ray images, allowing it to distinguish subtle indicators of COVID-19 effectively. Furthermore, this work discusses the potential of integrating these AI-driven diagnostic tools into existing healthcare infrastructures to enhance early detection and treatment of COVID-19. The findings indicate that leveraging artificial intelligence in medical imaging can substantially aid in managing and controlling disease outbreaks, ultimately contributing to better health outcomes.
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Study of the behavior of selected rocks at high temperatures
Holanec, Aleš ; Sitek,, Libor (referee) ; Bodnárová, Lenka (advisor)
The diploma thesis deals with the observation and diagnostics of transformations that take place in stone slabs of different mineralogical composition during heating to high temperatures. The theoretical part of the thesis deals with a summary of available information about selected types of rocks. Furthermore, it presents findings from the experimental thermal loading of a granite sample. The principles and instruments of laboratory experimental methods that are used to judge the resistance of rocks are described in detail. In the experimental part, the testing methodology is proposed, the procedure of production of rock test specimens is described and the way in which the thermal loading of stone slabs took place is explained. The results of the experiments are shown in the tables and graphs. A comparison of individual tests and properties of selected samples and evaluation of the obtained results is performed.
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Study of resistance of selected aggregates to high temperatures
Holanec, Aleš ; Sitek,, Libor (referee) ; Bodnárová, Lenka (advisor)
The thesis deals with the monitoring of changes taking place in natural stones and aggregates under high temperatures. The theoretical part of the work provides information about basic groups of rocks, the technology of processing the natural stone and primarily about the study of behavior of natural stone and aggregates at high temperatures. Also some findings from different research works aimed at monitoring the behavior of rocks at high temperatures are presented. In the experimental part a selection of rocks with a higher expected resistance to high temperatures is made, including detailed information and description. Further in this section we provide the comparison of the properties of individual rocks. Later we describe the most important methods of testing, the procedure of preparation of rock samples and we design an experiment to use for evaluation of resistance of rocks and aggregates under high temperatures.
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Study of the behavior of selected rocks at high temperatures
Holanec, Aleš ; Sitek,, Libor (referee) ; Bodnárová, Lenka (advisor)
The diploma thesis deals with the observation and diagnostics of transformations that take place in stone slabs of different mineralogical composition during heating to high temperatures. The theoretical part of the thesis deals with a summary of available information about selected types of rocks. Furthermore, it presents findings from the experimental thermal loading of a granite sample. The principles and instruments of laboratory experimental methods that are used to judge the resistance of rocks are described in detail. In the experimental part, the testing methodology is proposed, the procedure of production of rock test specimens is described and the way in which the thermal loading of stone slabs took place is explained. The results of the experiments are shown in the tables and graphs. A comparison of individual tests and properties of selected samples and evaluation of the obtained results is performed.
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