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
