Machine Learning Based Lung Cancer Classification in Histopathological Images- A Survey

Abstract - Examining histopathology slides visually is a key technique utilized by pathologists to determine the stage, type, and subtype of lung tumors. The most common subtypes of lung cancer are adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), and distinguishing between them necessitates careful visual evaluation by a skilled pathologist. The deep learning and machine learning techniques used to categorize histopathological lung cancer images are thoroughly examined in this work to aid in diagnosing lung cancer. Digital tissue pathology improves diagnosis accuracy and gives the pathologist more detail and better image quality with multiple viewing options and team annotations. Histopathological images are very useful for examining the conditions of different biological structures and diagnosing diseases like cancer. Key Words: Lung cancer, Machine Learning, Histopathological Image, Deep Learning