Deep Learning-Driven Liver Cancer Detection Enhanced by Particle Swarm Optimization (PSO) Algorithm
Keywords:
Liver Cancer Detection, Particle Swarm Optimization (PSO), Convolutional Neural Network (CNN), Computer-Aided Diagnosis (CAD), Image Segmentation, Fuzzy C-Means (FCM), Feature Extraction, Gray Level Co-occurrence Matrix (GLCM), Medical Image AnalysisAbstract
The Abstract— The early and accurate detection of liver cancer is paramount for effective diagnosis and treatment planning. This paper proposes a robust computer-aided diagnosis (CAD) framework for the classification of liver tumors by integrating a Convolutional Neural Network (CNN) with Particle Swarm Optimization (PSO). The implemented MATLAB pipeline begins with the acquisition of liver images, which are first enhanced through pre-processing techniques to improve quality and reduce noise. Subsequently, tumor regions are precisely delineated using Fuzzy C-Means (FCM) clustering for segmentation. Critical texture features are then extracted from the segmented regions using the Gray Level Co-occurrence Matrix (GLCM). The PSO algorithm is employed to optimize this feature set, selecting the most discriminative attributes to improve classification efficiency. The optimized features are used to train a CNN classifier for the binary classification of tumors into benign or malignant categories. The proposed model was rigorously evaluated, demonstrating high performance with accuracies of 94.99% for benign and 94.19% for malignant cases. Metrics including sensitivity, specificity, and precision further confirm the system's robustness and reliability. The synergy of PSO-based feature optimization and deep learning classification presents a powerful and efficient tool for assisting clinicians in making informed diagnostic decisions.
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