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Mass Detection in Lung CT Images Using Region Growing Segmentation and Decision Making Based on Fuzzy Inference System and Artificial Neural Network


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Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancers. Detecting and curing the disease in the early stages provides the patients with a high chance of survival. This work aims at detecting lung nodules automatically through computerized tomography (CT) image. Accordingly, this article aim at presenting a method to improve the efficiency of the lung cancer diagnosis system, through proposing a region growing segmentation method to segment CT scan lung images. Afterwards, cancer recognition are presenting by Fuzzy Inference System (FIS) for differentiating between malignant, benign and advanced lung nodules. In the following, this paper is testing the diagnostic performances of FIS system by using artificial neural networks (ANNs). Our experiments show that the average sensitivity of the proposed method is 95%.

Item Type: Journal article
Uncontrolled Keywords: Segmentation, Fuzzy Systems, Artificial neural networks, Cancer Detection
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > RB Pathology
Divisions: Schools > Centre for Business, Information Technology and Enterprise > School of Information Technology
Depositing User: Reza Rafeh
Date Deposited: 10 Jan 2017 00:58
Last Modified: 21 Jul 2023 04:34

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