A Review on DeepLungNet: CNN-Based Lung Cancer Detection Techniques Using CT Images

The disease known as lung cancer, which is common and frequently deadly, starts in the cells of the lungs and causes symptoms like exhaustion, chest pain, and persistent coughing. Since small cell lung cancer (SCLC) is less frequent but more dangerous than non-small cell lung cancer (NSCLC), knowledge of its causes, including smoking, pollution, and genetic factors, is essential. Survival rates are greatly increased by early identification by about 20%. This study comprehensively analyzes deep learning and machine learning models-based lung cancer prediction methods from 2016 to 2023. The review emphasizes how well these models work to achieve greater accuracy. Expanding on this, a new method is suggested that combines data augmentation and denoising preprocessing methods with a CNN in a ResNet architecture for classification. The goal of this hybrid model is to maintain and improve accuracy levels in the identification of benign and malignant lung pictures. This methodology offers promising promises for more accurate and reliable lung cancer prediction by utilizing advances in both preprocessing and classification techniques.