Enhanced Lung Cancer Detection via Modified Fuzzy Analytic Hierarchy Process and FuzSquResMobileNet

Abstract

Lung cancer emphasizes the vital need of early detection in order to increase survival rates due to its high incidence of death worldwide. An effective methodology for lung cancer detection is proposed utilizing the Modified Fuzzy Analytic Hierarchy Process, a hybrid optimization algorithm. Gaussian filter and a modified Contrast Limited Adaptive Histogram Equalization (CLAHE) are employed for reliable pre-processing. This enhances the image quality and facilitates additional analysis. A novel hybrid ChimpGaz optimization that combines the optimizations of Gazelle and Chimp is employed to fine-tune the Mask R-CNN algorithm for Region of Interest (ROI) identification. This guarantees ideal parameter configurations for precise ROI division. A wide range of descriptors are used in feature extraction, such as shape features (area, circularity, and perimeter), geometric features (centroid, Euler number, and convexity), color features and texture features (Local Directional Patterns, or LDP, and Gabor Local Binary Patterns, or GLBP). These features are ranked using a Modified Fuzzy Analytic Hierarchy Process (AHP) that includes sigmoid membership functions customized for every feature. The hybrid optimization algorithm improves membership functions even more, strengthening the ranking system's capacity for discrimination. The proposed detection model, called "FuzSquResMobileNet," combines a sophisticated ensemble of pre-trained deep learning models (SqueezeNet, ResNet101, MobileNetv2) with fuzzy logic-based preliminary detection. Overall detection accuracy is improved by this fusion because it guarantees a thorough approach to feature extraction. Through incorporating fuzzy logic and deep learning, the methodology provides a promising avenue for effective and precise lung cancer detection, contributing to early intervention and improved patient outcomes.