A Novel Method to Detect Lung Cancer using Deep Learning

Abstract

Lung cancer is a type of cancer that starts in the cells of the lungs, and it is one of the leading causes of cancer- related deaths worldwide. It is often caused by smoking, exposure to radon gas, and exposure to certain toxins and pollutants in the environment. Risk factors for lung cancer include smoking tobacco, exposure to the smoke exhaled by smokers, lung cancer in individual with a family history of the disease, and people have damaged lung, such as COPD. Diagnosis of lung cancer typically involves a combination of imaging tests, such as CT scans and X-rays, and biopsy. detection of lung cancer allows for more options in terms of treatment and a better chance of curing the cancer. In general, studies on this topic aim to use advanced deep learning techniques have the potential to improve the accuracy and efficiency of lung cancer detection and diagnosis, which can ultimately lead to better patient outcomes. These techniques involves both CNNs and DBNs have shown great promise in the analysis of medical imaging data for the detection and diagnosis of lung cancer, as they are able to automatically identify and classify patterns in the images that are indicative of lung cancer. These techniques have the potential to improve the accuracy and efficiency of lung cancer diagnosis. The recent techniques are GPT -3, Generative adversarial network, Deep reinforcement learning. Here, CNN algorithm is used to detect lung cancer earlier, they are able to automatically identify and classify patterns in medical images that are indicative of lung cancer. CNN that are particularly well-suited for image analysis because they can learn to recognize features in images and gives accuracy that are important for classification and detection.