- Tytuł:
- Denoising and Analysis Methods of Computer Tomography Results of Lung Diagnostics for Use in Neural Network Technology
- Autorzy:
-
Slavova, Oleksandra
Lebid, Solomiya - Powiązania:
- https://bibliotekanauki.pl/articles/1833888.pdf
- Data publikacji:
- 2020
- Wydawca:
- Polska Akademia Nauk. Oddział w Lublinie PAN
- Tematy:
-
computed tomography
CT scans analysis
convolutional neural network
image clustering
image denoising
k-means clustering - Opis:
- Any type of biomedical screening emerges large amounts of data. As a rule, these data are unprocessed and might cause problems during the analysis and interpretation. It can be explained with inaccuracies and artifacts, which distort all the data. That is why it is crucial to make sure that the biomedical information under analysis was of high quality to omit to receive possibly wrong results or incorrect diagnosis. Receiving qualitative and trustworthy biomedical data is a necessary condition for high-quality data assessment and diagnostics. Neural networks as a computing system in data analysis provide recognizable and clear datasets. Without such data, it becomes extremely difficult to make a diagnosis, predict the course of the disease, and treatment result. The object of this research was to define, describe, and test a new approach to the analysis and preprocessing of the biomedical images, based on segmentation. Also, it was summarized different metrics for assessing image quality depending on the purpose of research. Based on the collected data, the advantages and disadvantages of each of the methods were identified. The proposed method of analysis and noise reduction was applied to the results of computed tomography lungs screening. Based on the appropriate evaluation metrics, the obtained results were evaluated quantitatively and qualitatively. As a result, the expediency of the proposed algorithm application was proven.
- Źródło:
-
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2020, 9, 1; 19--24
2084-5715 - Pojawia się w:
- ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
- Dostawca treści:
- Biblioteka Nauki
Artykuł