LUNG CANCER DETECTION TECHNIQUE BASED ON SURF DESCRIPTOR AND KNN ALGORITHMS

Abstract

In this century, lung cancer is undoubtedly one of the major serious health problems, and one of the leading causes of death for women and men worldwide. Despite advances in treating lung cancer with unprecedented products of pharmaceutical and technological advances, mortality and morbidity rates remain a major challenge for oncologists and cancer biologists. Thus, there is an urgent need to provide early, accurate, and effective diagnostic techniques to improve the survival rate and reduce morbidity and mortality related to lung cancer patients. Therefore, in this paper, an effective lung cancer screening technique is proposed for the early detection of risk factors for lung cancer. In this proposed technique, the powerful acceleration feature Speeded up robust feature (SURF) was used to extract the features. One of the machine learning methods was used to detect cancer by relying on the k nearest neighbor (KNN ) method, where the experimental results show an effective way to discover SURF features and tumor detection by relying on neighborhoods and calculating the distance using KNN. As a result, a high system sensitivity performance success rate of 96% and a system accuracy of 99% has been achieved.