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.
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