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Objectives: Early and accurate detection of lung cancer from histopathological images is time-consuming and often requires additional staining, leading to delayed diagnosis. This study aims to develop an automated and optimized framework for reliable lung cancer detection from H & E-stained histopathological images using advanced segmentation, feature optimization, and classification techniques.

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Materials and Methods: The proposed framework begins with image preprocessing using an adaptive median filter to suppress noise while preserving structural details. A novel Hybrid Simple Linear Iterative Clustering – K-Means – Fuzzy C Means (SLIC–KM–FCM) based segmentation approach is employed to extract diagnostically relevant regions. Feature dimensionality reduction is performed using bio-inspired optimization algorithms, namely Whale Optimization Algorithm (WOA) and Harmony Search Optimization Algorithm (HSOA). The most discriminative features are selected using Monkey Search Algorithm (MSA) and T-statistics (T-Stat). These selected features are provided as input to multiple classifiers, including SVM, KNN, RF, DT, SDC, MLP, and BLDC. Classifier performance is evaluated using standard metrics, both with and without hyper-parameter tuning using Grid Search (GS) and Stochastic Gradient Descent (SGD).

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Results: Without hyper-parameter tuning, the SVM classifier combined with WOA-based feature extraction and MSA-based feature selection achieved an accuracy of 87.50%. The application of hyper-parameter optimization significantly improved classification performance. The highest accuracy of 93.75% was obtained using the BLDC classifier with HSOA-based feature extraction, T-Stat feature selection, and Grid Search optimization.

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Conclusion: The proposed hybrid optimization-driven framework effectively improves lung cancer classification from histopathological images. The integration of advanced segmentation, bio-inspired feature optimization, and hyper-parameter tuning demonstrates strong potential for developing robust computer-aided diagnostic systems in digital pathology.

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Adaptive Median Filter improves histopathological lung image quality.

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Hybrid SLIC–KM–FCM based segmentation enhances histopathological region analysis.

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Bio-inspired optimization improves feature extraction and selection accuracy.

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Hyper-parameter tuning (HPT) significantly improves classifier performance.

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BLDC classifier with GSO HPT achieves 93.75% accuracy on histopathological images.