Acute interstitial pneumonia image enhancement using fuzzy partial transforms

• Published in: Applied geomatics Vol. 16; no. 1; pp. 35 - 39
• Main Author: Kadham, Shaymaa Maki
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-03-2024; Springer Nature B.V
• Subjects: Earth and Environmental Science; Geographical Information Systems/Cartography; Geography; Geophysics/Geodesy; Image enhancement; Image processing; Integral equations; Laplace transforms; Leukemia; Mathematical models; Measurement Science and Instrumentation; Medical sciences; Original Paper; Partial differential equations; Pneumonia; Remote Sensing/Photogrammetry; Surveying; transform; Mathematical modeling; Classification of leukemia model; Partial differential equations; Process image of acute interstitial pneumonia model; Fuzzy partial
• ISSN: 1866-9298; 1866-928X
• DOI: 10.1007/s12518-023-00509-8

In this paper, we present a new application to medical science and engineering fields that are heavily dependent on fuzzy partial differential integral equations. Acute interstitial pneumonia is the most common cancer in people under the age of 20, and accounts for about 6% of all cancers diagnosed in adults. In recent years, there has been increasing interest in using mathematical models in oncology to draw conclusions and predictions, providing a complementary picture to experimental biomedical models. This study proposes to use the new fuzzy partial transform (
ψ
) to solve the proposed leukemia model, which is a new approach to solve such concepts and image processing of affected cells by converting the idea into a filter, optimizing radiographs, and distinguishing parameters. The fuzzy partial differential equation with generalized Hukuhara differantiation is solved analytically using the fuzzy laplace transform. The proposed transform proved to be highly efficient compared to the fuzzy partial Laplace transform in clarity. The picture and finally the classification of the stages of infection and its impact on the patient and his ability to respond to treatment.