Enhanced Superpixel-Guided ResNet Framework with Optimized Deep-Weighted Averaging-Based Feature Fusion for Lung Cancer Detection in Histopathological Images

<b>Background/Objectives:</b> Lung cancer is a leading cause of cancer-related mortalities, with early diagnosis crucial for survival. While biopsy is the gold standard, manual histopathological analysis is time-consuming. This research enhances lung cancer diagnosis through deep learning-based feature extraction, fusion, optimization, and classification for improved accuracy and efficiency. <b>Methods:</b> The study begins with image preprocessing using an adaptive fuzzy filter, followed by segmentation with a modified simple linear iterative clustering (SLIC) algorithm. The segmented images are input into deep learning architectures, specifically ResNet-50 (RN-50), ResNet-101 (RN-101), and ResNet-152 (RN-152), for feature extraction. The extracted features are fused using a deep-weighted averaging-based feature fusion (DWAFF) technique, producing ResNet-X (RN-X)-fused features. To further refine these features, particle swarm optimization (PSO) and red deer optimization (RDO) techniques are employed within the selective feature pooling layer. The optimized features are classified using various machine learning classifiers, including support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor (KNN), SoftMax discriminant classifier (SDC), Bayesian linear discriminant analysis classifier (BLDC), and multilayer perceptron (MLP). A performance evaluation is performed using K-fold cross-validation with K values of 2, 4, 5, 8, and 10. <b>Results:</b> The proposed DWAFF technique, combined with feature selection using RDO and classification with MLP, achieved the highest classification accuracy of 98.68% when using K = 10 for cross-validation. The RN-X features demonstrated superior performance compared to individual ResNet variants, and the integration of segmentation and optimization significantly enhanced classification accuracy. <b>Conclusions:</b> The proposed methodology automates lung cancer classification using deep learning, feature fusion, optimization, and advanced classification techniques. Segmentation and feature selection enhance performance, improving diagnostic accuracy. Future work may explore further optimizations and hybrid models.