A Lightweight Hybrid CNN–DBN Framework for Lung Cancer Classification Using CT Images

The current research work proposes an automated lightweight hybrid CNN-DBN framework for the classification of lung cancer from the CT images. The framework consists of CLAHE for enhancing images, CNN for spatial feature extraction, DBN for hierarchical representational learning and Logistic Regression for classification that ensures efficient and accurate diagnosis. A low-confidence sample refinement strategy is presented which filters out the samples that the model does not predict accurately, and therefore improves the accuracy of the prediction without re-training the model. The proposed framework yield 98.80% classification accuracy, which shows the efficacy of using both supervised and unsupervised feature learning in medical image analysis. The use of lightweight AI-based CAD systems for accurate and efficient lung cancer screening were emphasized.