A Hybrid Deep Learning Approach for Early Detection and Classification of Lung Cancer Using the Pelican Optimization Algorithm

Lung cancer is a global health challenge that requires advanced diagnostic tools for early detection and classification. This study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for accurate and timely identification of lung cancer in medical imaging data The model uses Convolutional Neural Networks (CNN) for spatial features extraction and Recurrent Neural Networks (RNN) for temporal understanding. It achieves an accuracy of 97.3%, demonstrating its efficacy in identifying subtle lung cancer patterns. The Pelican Optimization Algorithm is employed to fine-tune the parameters of the hybrid model, enhancing its overall performance. The comprehensive experimental evaluations show that the hybrid CNN-RNN model, enriched by the Pelican Optimization Algorithm, outperforms existing approaches in early detection and classification of lung cancer. The combined strengths of spatial and temporal feature extraction, coupled with the optimization capabilities of the Pelican algorithm, make the model a promising tool for clinicians and healthcare professionals in the fight against lung cancer.