Deep Learning Transformations in Healthcare: Emphasizing Lung Cancer Diagnosis and Prognosis

Abstract

The rapid advancement of deep learning technologies is transforming the healthcare industry, offering significant potential to enhance patient outcomes, particularly in the field of oncology. This study delves into the application of deep learning techniques in diagnosing and predicting lung cancer, emphasizing the transformative impact of these technologies. Convolutional Neural Networks (CNNs) are utilized to identify lung nodules and cancers through medical imaging, while Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs) are applied to forecast patient survival based on longitudinal data. We explore the use of ensemble models to increase prediction accuracy and examine multi-model ensembles that improve imaging for lung cancer prognosis and risk assessment using low-dose CT scans. Furthermore, this study assesses the effects of deep learning in more general healthcare contexts, offering insights into its drawbacks and possibilities for clinical integration. In conclusion, our survey highlights the promise of deep learning to improve the detection and treatment of lung cancer while drawing attention to persistent issues like data privacy and interpretability that must be resolved for wider clinical usage.

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