Published Paper Details:

Lung cancer is a leading cause of cancer-related deaths worldwide. Early detection and accurate prediction of malignancy in small cell lung nodules is crucial for improving patient outcomes. Deep learning techniques have shown promise in analyzing medical imaging data for lung cancer prediction in recent years. This paper comprehensively reviews the latest deep-learning approaches applied to predicting lung cancer in small-cell malignant lung nodules. We discuss various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models. We also examine the datasets commonly used for training and evaluating these models, as well as the performance metrics employed. Furthermore, we highlight the challenges and limitations of current deep-learning techniques and outline potential future research directions. This review aims to provide researchers and practitioners with a thorough understanding of the state-of-the-art in deep learning for lung cancer prediction, facilitating the development of more accurate and robust models for clinical decision support.