Automatic Diagnosis of Lung Diseases (Pneumonia, Cancer) with given Reliabilities on the Basis of an Irradiation Images of Patients

K.J.  Kachiashvili; J.K.  Kachiashvili; V.V.  Kvaratskhelia

doi:10.6000/1929-6029.2024.13.07

Authors

K.J. Kachiashvili Faculty of Informatics and Control Systems, Georgian Technical University, 77, st. Kostava, Tbilisi, 0160, Georgia; Department of Probability Theory and Mathematical Statistics of the I. Vekua Institute of Applied Mathematics, Tbilisi State University, Tbilisi, Georgia and Department of Informatics of the Muskhelishvili Institute of Computational Mathematics, Georgian Technical University, Tbilisi, Georgia https://orcid.org/0000-0002-5313-5991
J.K. Kachiashvili Faculty of Informatics and Control Systems, Georgian Technical University, 77, st. Kostava, Tbilisi, 0160, Georgia and Department of Informatics of the Muskhelishvili Institute of Computational Mathematics, Georgian Technical University, Tbilisi, Georgia
V.V. Kvaratskhelia Faculty of Informatics and Control Systems, Georgian Technical University, 77, st. Kostava, Tbilisi, 0160, Georgia and Department of Informatics of the Muskhelishvili Institute of Computational Mathematics, Georgian Technical University, Tbilisi, Georgia

DOI:

https://doi.org/10.6000/1929-6029.2024.13.07

Keywords:

Automatic diagnosis, lung diseases, pneumonia, cancer, making decision, simulation, artificial intelligence

Abstract

The article proposes algorithms for the automatic diagnosis of human lung diseases pneumonia and cancer, based on images obtained by radiation irradiation, which allow us to make decisions with the necessary reliability, that is, to restrict the probabilities of making possible errors to a pre-planned level. Since the information obtained from the observation is random, Wald’s sequential analysis method and Constrained Bayesian Method (CBM) of statistical hypothesis testing are used for making a decision, which allow us to restrict both types of possible errors. Both methods have been investigated using statistical simulation and real data, which fully confirmed the correctness of theoretical reasoning and the ability to make decisions with the required reliability using artificial intelligence. The advantage of CBM compared to Wald’s method is shown, which is expressed in the relative scarcity of observation results needed to make a decision with the same reliability. The possibility of implementing the proposed method in modern computerized X-ray equipment due to its simplicity and promptness of decision-making is also shown.

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