A Neuro-Fuzzy Based System for the Classification of Cells as Cancerous or Non-Cancerous

Omotosho, Adebayo and Asani, E. Oluwatobi and Ogundokun, Roseline Oluwaseun and Ananti, Emmanuel Chukwuka and Adegun, Adekanmi (2018) A Neuro-Fuzzy Based System for the Classification of Cells as Cancerous or Non-Cancerous. International Journal of Medical Research & Health Sciences, 7 (5). pp. 155-166. ISSN 2319-5886

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Abstract

Objectives: In this study, we developed a neuro-fuzzy based system for classification of cancerous and non-cancerous lung cells. Methods: Images were pre-processed using median filter algorithm, segmented using marker-controlled watershed algorithm, and were extracted using gray-level co-occurrence matrix. A hybridized diagnosis system that made use of neural network and fuzzy logic for classification of lung cells into cancerous and non-cancerous cells is modelled. Computed tomography (CT) scan image dataset of the lung was downloaded from The Cancer Imaging Archive dataset. Neural network performed the training and classification of the lung cells with back-propagation algorithm, while the cancerous cells were passed into fuzzy inference system to determine the lung cancer stage. Results: Our system was able to successfully classify the imported CT scan images into normal or abnormal with considerably high accuracy of 70% and precision of 89%. This system can support physicians in decision making when diagnosing cancer

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General)
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Dr Adebayo Omotosho
Date Deposited:	30 Nov 2018 10:59
Last Modified:	30 Nov 2018 10:59
URI:	https://eprints.lmu.edu.ng/id/eprint/1452

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