Detection of Lung Cancer using Image Processing Methods

The largest cause of cancer-related fatalities globally is lung cancer. Lung cancer treatment results and survival rates can be considerably enhanced by early identification and diagnosis. Image processing techniques have attracted attention as useful tools for the early identification and diagnosis of lung cancer because of improvements in medical imaging technology. This review study offers a thorough examination of the various image-processing methods used in lung cancer diagnosis. The importance of early detection and the difficulties in conventional diagnosis techniques are covered in the first section of the paper. The potential of image processing methods to solve these issues and boost diagnostic precision is then highlighted. The review discusses several feature extraction, segmentation, and classification techniques used in lung cancer diagnosis. The precise detection and delineation of lung tumors from computed tomography (CT) scan or chest X-ray images is investigated using image segmentation algorithms. To get pertinent data and traits from the segmented tumor areas, feature extraction techniques are next examined. In the end, classification methods are looked at for separating benign and malignant tumors based on the data retrieved. The research also examines the combination of image processing methods with machine learning and deep learning algorithms for improved lung cancer diagnosis. It draws attention to the benefits and drawbacks of these algorithms in terms of increasing diagnostic precision and lowering false-positive or false-negative outcomes. The study concluded with a discussion of the potential applications of image-processing techniques in the diagnosis of lung cancer. It emphasizes how computer-aided diagnostic methods and artificial intelligence have the potential to revolutionize the detection and treatment of lung cancer. In conclusion, this paper offers a thorough overview of the image processing techniques used in lung cancer diagnosis. It clarifies how these methods could aid in the early detection of lung cancer, improve the design of the appropriate course of therapy, and eventually improve patient outcomes.

Keywords: Image processing, Lung cancer, X-rays image