Lung Cancer Diagnosis Using X-Ray and CT Scan Images Based on Machine Learning Approaches

Lung cancer is one of the biggest threats to mankind. The number of patients who died from lung cancer is too large compared to the total number of cancer diagnoses. Lung cancer is uncontrollable cell proliferation in the lungs and can be recognized as a nodule, which might be benign or malignant. A nodule is a white-colored area on the lungs that can be seen on an X-ray or CT scan image. With the advancement of technology, many interdisciplinary domains are working together. Technologies such as machine learning (ML) have greatly assisted in lung cancer diagnosis using an imaging modality. An X-ray and/or CT scan image is used as the input for ML techniques. It is processed using image processing techniques, and the findings are then classified using ML algorithms that are presented in this study through a pipeline of ML. According to the specifications of the ML pipeline, several intermediate processes such as image preprocessing, lung segmentation and enhancement, nodule detection, feature extraction, and classification were also briefly described with their techniques. The entire study investigates various ML approaches used in lung cancer diagnosis using X-ray and CT scan images.