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Matlab Based Intelligent Fuzzy System for Early Detection of Lung Cancer

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Matlab Based Intelligent Fuzzy System for Early Detection of Lung Cancer


Sakshi Ramdas Jarhad | Sakshi Aaba Chakane | Yogita Raghunath Temgire | Rahul Sitaram Bansode



Sakshi Ramdas Jarhad | Sakshi Aaba Chakane | Yogita Raghunath Temgire | Rahul Sitaram Bansode "Matlab Based Intelligent Fuzzy System for Early Detection of Lung Cancer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-10 | Issue-3, June 2026, pp.96-103, URL: https://www.ijtsrd.com/papers/ijtsrd125241.pdf

Lung cancer is a major contributor to cancer-related mortality worldwide, underscoring the critical need for precise diagnostic tools to facilitate early detection and accurate staging. Conventional diagnostic methods, which involve the manual interpretation of CT scans, are susceptible to human error and operational inefficiencies. This paper details the development and implementation of a Fuzzy Logic-Based Lung Cancer Detection and Staging System. This system is designed to analyze CT scan images for the identification, classification, and staging of lung tumors. The proposed system encompasses several stages: image preprocessing to enhance visual quality, feature extraction to identify key tumor characteristics such as dimensions, form, and texture, and fuzzy logic classification for nuanced categorization. Unlike systems employing binary classification, this approach assigns membership values to tumor attributes, allowing for a more flexible and precise method of cancer staging. The system concludes with a Graphical User Interface (GUI) that enables users to upload images, review classification outcomes, and understand tumor staging, thereby improving its practical utility. Performance evaluation using the LUNA16 dataset revealed high accuracy, precision, recall, and F1-score. These findings highlight the efficacy of fuzzy logic in managing the inherent uncertainties in medical imaging, thereby enhancing early detection capabilities and supporting clinicians in making well-informed treatment decisions. This system represents a substantial advancement in lung cancer diagnostics, effectively integrating computational adaptability with clinical precision.

Lung cancer detection, cancer staging, fuzzy logic, CT scan analysis, feature extraction, tumor classification, medical imaging.


IJTSRD125241
Volume-10 | Issue-3, June 2026
96-103
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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