Deep Learning Approach for Early Stage Lung Cancer Detection
This addresses the critical need for early detection to improve survival rates for lung cancer patients, but appears incremental as it builds on existing deep learning methods without specifying novel breakthroughs.
The paper tackled the problem of late lung cancer diagnosis by proposing a deep-learning model for early prediction and diagnosis from CT scans, achieving high accuracy.
Lung cancer is the leading cause of death among different types of cancers. Every year, the lives lost due to lung cancer exceed those lost to pancreatic, breast, and prostate cancer combined. The survival rate for lung cancer patients is very low compared to other cancer patients due to late diagnostics. Thus, early lung cancer diagnostics is crucial for patients to receive early treatments, increasing the survival rate or even becoming cancer-free. This paper proposed a deep-learning model for early lung cancer prediction and diagnosis from Computed Tomography (CT) scans. The proposed mode achieves high accuracy. In addition, it can be a beneficial tool to support radiologists' decisions in predicting and detecting lung cancer and its stage.