A Robust Framework for Lung Cancer Prediction Using Deep Convolutional Neural Networks and Advanced Image Processing Techniques
Keywords:
Ant Colony Optimization, Feature selection, Fuzzy-based median filter, Lung cancer predictionAbstract
Lung cancer is a malignant condition characterized by the uncontrolled growth of abnormal cells in the lung tissues. It is a leading cause of cancer-related mortality worldwide, attributed to factors such as tobacco smoke exposure, environmental pollutants, and genetic predisposition. This study presents an integrated framework for lung cancer prediction, incorporating a series of advanced image processing and machine learning techniques. The proposed approach begins with noise removal using a fuzzy-based median filter to enhance image quality. Circular Local Binary Pattern (CLBP) is then employed for feature extraction, capturing essential texture information. Segmentation is performed using a threshold-based mutilate segmentation method to isolate potential cancerous regions. For feature selection, an innovative strategy combining Ant Colony Optimization and correlation negation is introduced, optimizing the relevant feature set while minimizing redundancies. The final stage involves classification using Adenocarcinoma Deep Convolutional Neural Network (ADCNN), leveraging its ability to automatically learn hierarchical representations for accurate lung cancer prediction. The framework is designed to improve predictive performance and contribute to early detection, thus enhancing patient outcomes in lung cancer management.
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