The Deep Learning ResNet101 and Ensemble XGBoost Algorithm with Hyperparameters Optimization Accurately Predict the Lung Cancer

Lung cancer is the most common and second leading cause of cancer with lowest survival rate due to lack of efficient diagnostic tools. Currently, researchers are devising artificial intelligence based tools to improve the diagnostic capabilities. The machine learning (ML) requires hand-crafted features to train the algorithms. To extract most relevant features is still a challenging task in the field image processing. We first extracted the texture gray level co-occurrence matrix features. We fed these features to traditional ML algorithms such as k-nearest neighbor (KNN) and support vector machine (SVM). The SVM yielded an accuracy of 83.0%, whereas KNN produced an accuracy of 97.0%. We then optimized and employed the ensemble extreme boosting (XGBoost) algorithm, which improved the detection performance with precision, recall, and accuracy of 100%. We also optimized and employed the deep learning ResNet101 to distinguish the small cell cancer from non-small cell lung cancer and obtained the 100% performance with these evaluation performance measures. The results revealed that proposed approach is more robust than traditional ML algorithms. Based on these results, the proposed methodology can be very helpful in the early detection and treatment of lung cancer for better diagnosis system.