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Fuzzy Analysis of CT-scan Images of Hepatic Parasitosis
Current Issue
Volume 6, 2019
Issue 3 (September)
Pages: 83-87   |   Vol. 6, No. 3, September 2019   |   Follow on         
Paper in PDF Downloads: 17   Since Jul. 5, 2019 Views: 904   Since Jul. 5, 2019
Authors
[1]
Bouharati Imene, Radiology Department, UFAS Ferhat Abbas Sétif1 University, Setif, Algeria; Laboratory of Intelligent Systems, UFAS Ferhat Abbas Sétif1 University, Setif, Algeria.
[2]
Bounechada Mustapha, Department of Biology and Animal Physiology, UFAS Ferhat Abbas Sétif1 University, Setif, Algeria.
[3]
Babouche Farid, Radiology Department, UFAS Ferhat Abbas Sétif1 University, Setif, Algeria.
[4]
Bouharati Khaoula, Laboratory of Health and Environment, UFAS Ferhat Abbas Sétif1 University, Setif, Algeria.
[5]
Bouharati Saddek, Laboratory of Intelligent Systems, UFAS Ferhat Abbas Sétif1 University, Setif, Algeria.
Abstract
Background: Hepatobiliary parasites are often the cause of a granulomatous reaction, they are responsible of radiological confusion mimicking tumor lesions. In imaging radiology, this is characterized by the absence of lesional contrast, the presence of calcification and the presence of hepatic granulomas. The example of toxocariasis that is caused by the toxocaracani or Taticati parasite manifested in ultrasound and CT-scan (computerized tomography), by multiple small intrahepatic lesions, oval or elongated and poorly defined. This result is hypo or isoechogenic. Objectif: If the confirmation of the diagnosis passes by a liver biopsy on specific eosinophilic granulomas we propose in this study the identification of the granulomas of origin toxocarose and to differentiate them from the tumoral forms. Methods: Given the complexity of the system, the factors taken into account are the volume distribution of the density, the size, and the shape of the contour, the location and the living conditions of the patient. These variables are considered input variables to a fuzzy system. The output variable expresses the nature of the granuloma. As the fuzzy logic that is a subfield of artificial intelligence deals with the uncertain and imprecise, its application in this area proves adequate. A database is established from the real cases in the form (If... Then). Conclusion: Once the fuzzy algorithm is done, it allows identifying the nature of the tumoral granulomas or toxocariasis before biological techniques confirmation.
Keywords
Hepatobiliary, Parasites, Toxocariasis, CT-Scan, Granulomas, Tumor, Fuzzy Logic
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BouharatiImene, BaboucheFarid, BouharatiSaddek. Radiography and Risk Factors of Lung Cancer: Modeling Using an Intelligent System. Int J RadiolRadiatTher 2017, 3 (5): 00074.
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BouharatiKhaoula, Kara Lamia, ZaidiZoubida, BouaoudSouad, BoukhaoubaHafida, BouharatiSaddek, Hamdi-CherifMokhtar. Age Characteristics of Patients with Diagnosis ofLeukemia in Setif-Algeria: Intelligent Modeling System. International Journal of Public Health Research. 2017; 5 (2): 31-35.
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