Combining Handcrafted Features and Deep Learning for Automatic Classification of Lung Cancer on CT Scans

Abstract

On a global scale, lung cancer is responsible for around 27% of all cancer fatalities. Even though there have been great strides in diagnosis and therapy in recent years, the five-year cure rate is just 19%. Classification is crucial for diagnosing lung nodules. This is especially true today that automated categorization may provide a professional opinion that can be used by doctors. New computer vision and machine learning techniques have made possible accurate and quick categorization of CT images. This field of research has exploded in popularity in recent years because of its high efficiency and ability to decrease labour requirements. Here, they want to look carefully at the current state of automated categorization of lung nodules. General-purpose structures are briefly discussed, and typical algorithms are described. Our results show deep learning-based lung nodule categorization quickly becomes the industry standard. Therefore, it is critical to pay greater attention to the coherence of the data inside the study and the consistency of the research topic. Furthermore, there should be greater collaboration between designers, medical experts, and others in the field.