A Review on Deep Learning Approaches for Early- Stage Lung Cancer Detection and Diagnosis

Because doctors must detect lung cancer early and patients must exhibit certain symptoms, lung cancer remains one of most common and deadly malignancies in the world. Patients need to undergo early-stage testing for their survival rate to improve but existing methods which include biopsy and manual CT image assessment show high levels of disruption and require lengthy time periods while also suffering from diagnostic pitfalls. Artificial intelligence recent innovations through deep learning technology now enable researchers to solve this problem by establishing systems which automatically detect cancerous patterns with high precision and rapid processing speed. Medical professionals now prefer Convolutional Neural Networks as their primary method for extracting advanced medical image data because traditional Support Vector Machines (SVMs) have become outdated. According to the study, sophisticated methods like transfer learning and attention processes combined with hybrid artificial intelligence models have enhanced system performance by providing better model understanding while reducing overfitting issues and improving model performance in new situations. This review conducts an in-depth analysis of deep learning methods which currently assist doctors in diagnosing and treating lung cancer by examining their advantages and disadvantages and their future development possibilities. The research demonstrates how deep learning transforms medical image processing to enhance early disease detection which leads to decreased patient deaths and improved treatment results.