Integrating Predictive Analytics and Deep Neural Networks for Early Lung Cancer Diagnosis

Abstract

One of the leading causes of cancer-related mortality globally is lung cancer; hence, early and effective screening methods are crucial. This work combines advanced deep learning models with predictive analytics to improve the early detection of lung cancer. The lung cancer histopathological images dataset is used to analyze histopathological slides and clinical data using a range of models, including convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM) networks, feedforward neural networks (FNN), and deep reinforcement learning (DRL). Because CNN can extract spatial characteristics, it performs better than the other models in accurately categorizing tissues that are malignant and those that are not. While FNN is a supplementary tool for incorporating non-image clinical metadata, LSTM and RNN models are investigated for their capacity to manage sequential patterns within patient data. By mimicking clinical operations, improving diagnostic accuracy, and lowering false positives, DRL streamlines decision-making processes. This study demonstrates the revolutionary potential of deep learning-powered predictive analytics in the early detection of lung cancer. These techniques open the door for AI-driven advancements in customized medicine and precision oncology by increasing diagnosis accuracy and facilitating prompt therapies. Prospective avenues for future research are provided by the further integration of hybrid systems and multimodal data.