A Review on Lung Cancer Detection and Classification Using Deep Learning Techniques

Cancer is a deadly illness affected by a confluence of genetic disorders and metabolic abnormalities. Cancer is the second biggest reason of mortality universal, with lung cancer having a substantially higher death rate than other types of cancer. The discovery of such nodes/cancer histopathologically is typically the maximum critical factor in selecting the optimal sequence of therapy. Initial disease detection on both fronts dramatically lowers the risk of death. To identify cancer, DL platforms are thought to be the best choice for detecting cancer in the most accurate and stress-free manner for clinicians. While other review publications have examined different system elements, this evaluation focuses on segmenting and categorising lung cancer. Specifically, in this review paper, research work has been selected based on detection and classification using different neural networks. Along with the information utilised for the study, tables have also been made to fully describe the vital processes of lung nodule identification and diagnosis. As a result, for readers this review gives a foundational understanding of the subject.