Deep Learning Algorithms for Predicting the Onset of Lung Cancer

EasyChair Preprint 13589

Abstract

Lung cancer is a major global health concern, and early detection plays a crucial role in improving patient outcomes. Deep learning algorithms have shown promising potential in predicting the onset of lung cancer, aiding in timely diagnosis and treatment. This paper presents an overview of deep learning algorithms employed for lung cancer prediction. The data collection and preprocessing phase involves gathering diverse data sources such as medical records, imaging data, and genetic information, followed by appropriate preprocessing techniques. Convolutional Neural Networks (CNNs) are utilized for analyzing lung images, while Recurrent Neural Networks (RNNs) capture temporal dependencies in sequential patient data. Autoencoders are employed to extract meaningful features, and Generative Adversarial Networks (GANs) generate synthetic data for augmenting the training set. Evaluation metrics and cross-validation techniques are discussed to assess model performance, and the challenges and limitations of deep learning in this context are outlined. Finally, future directions are highlighted, emphasizing the integration of multimodal data and collaborative research efforts to enhance lung cancer prediction. The potential of deep learning algorithms to improve early detection and prediction of lung cancer holds promise for advancing patient care and reducing the burden of this devastating disease.

Keyphrases: Computational Resources, Data Quality, class imbalance, data availability, generalization, interpretability

@booklet{EasyChair:13589,
  author    = {Elizabeth Henry},
  title     = {Deep Learning Algorithms for Predicting the Onset of Lung Cancer},
  howpublished = {EasyChair Preprint 13589},
  year      = {EasyChair, 2024}}