Enhanced Detection and Prediction of Lung Cancer using CNN and RNN Techniques on Text Dataset

Abstract

Lung cancer remains one of the leading causes of cancer-related mortality globally, highlighting the urgent need for precise and reliable detection methods. This research introduces a novel approach to improving lung cancer detection and prediction by combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) on an extensive text dataset. Our methodology tackles several common issues in current deep learning techniques used for lung cancer detection. A major problem is the dependence on small sample sizes, which often results in overfitting and limited generalization. To address this, we applied extensive data augmentation and transfer learning techniques, improving the model’s ability to perform well on new, unseen data. Additionally, we reduced the over-reliance on previous studies by developing innovative model architectures that merge CNNs’ ability to extract spatial features with RNNs’ capability to capture temporal dependencies, creating a more robust predictive framework. We also emphasized the importance of validating our model on independent datasets by rigorously testing it on a variety of external datasets, ensuring its robustness and generalizability. To address the resource-intensive nature of training deep learning models, we utilized advanced computational resources to optimize both model training and deployment efficiency. Another challenge is the potential bias in training data, which can lead to skewed predictions. We minimized these biases by carefully selecting and preprocessing our dataset, ensuring our model’s applicability across different populations. Moreover, incorporating longitudinal data allowed our model to better understand disease progression, enhancing long-term outcome predictions. Our system also considers practical aspects for clinical integration, ensuring that the models can be easily adopted in healthcare settings. By maintaining high-quality, standardized imaging and expanding our dataset to include more diverse samples, we increased the model’s robustness and reliability. In conclusion, our integration of CNN and RNN techniques,
along with advanced data handling strategies, offers a comprehensive solution for lung cancer detection and prediction. This approach addresses critical limitations of existing methods and paves the way for more effective clinical applications.