Study & Analysis of Lung Cancer Risk Prediction Techniques Using ML and DL Algorithms

Lung cancer remains a significant global health challenge as it causes most deaths around the world, emphasizing the need for effective early detection and treatment strategies. The human body is equipped with defense mechanisms to guard the lungs and respiratory system. While these mechanisms play a crucial role in safeguarding the lungs, they may not provide absolute immunity against the diverse range of diseases that can impact respiratory health. This may lead to cancerous tumor growth that can extend to different areas of the body causing lung cancer. The outlook for lung cancer is greatly influenced by the stage at which it is identified, with early detection significantly improving patient outcomes and survival rates. Therefore, there is a need for advanced techniques to enable early detection and accurate diagnosis of lung cancer. This project focuses on the development of an advanced approach using various Machine Learning techniques (applied on various basic symptoms). Further, understand Deep Learning techniques (applied on various Histopathological Images) are utilized for early-stage prediction of lung cancer. The objective is to formulate a reliable and accurate system that can assist healthcare proficient in diagnosing and classifying different types of lung cancer.