Hybrid Deep Learning Models for Accurate Early Detection of Lung Cancer from MRI Scans

Early identification of lung cancer is vital for improving patient outcomes, since it is a major contributor to cancer-related mortality, often resulting from late-stage diagnosis. This study addresses the challenge of accurately identifying early-stage lung cancer from MRI scans, where subtle tumors are often overlooked. Traditional diagnostic methods, reliant on manual interpretation and conventional machine learning models, fall short in handling the complexity of MRI data. In order to address these constraints, we suggest using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) for spatial feature extraction with Transformer networks for contextual analysis. This innovative approach significantly enhances the accuracy of early-stage lung cancer detection. Performance evaluation on extensive MRI datasets demonstrates that the hybrid model achieves an accuracy of 95%, a sensitivity of 93%, and a specificity of 96%, outperforming traditional diagnostic methods. The results highlight the potential of this hybrid model to revolutionize early detection strategies, ultimately improving treatment outcomes and survival rates for lung cancer patients.