Earlier, Lung cancer is the primary cause of cancer deaths worldwide among both men and women, with more than 1 million deaths annually. Lung Cancer have been widest difficulty faced by humans over recent couple of decades. When a person has lung cancer, they have abnormal cells that cluster together to form a tumor. A cancerous tumor is a group of cancer cells that can grow into and destroy nearby tissue. It can also spread to other parts of the body. There are two main types of lung cancer:1. Non-small cell lung cancer, 2. Small cell lung cancer. Non- small cell lung cancer has four main stages. In this research we are classifying four stages of lung cancer. Lung cancer detection at early stage has become very important. Currently many techniques are used based on image processing and deep learning techniques for lung cancer classification. For that lung patient Computer Tomography (CT) scan images are used to detect and lung nodules and classify lung cancer stage of that nodules. In this re- search we compare different Machine learning (SVM, KNN, RF etc.) techniques with deep learning (CNN, CDNN) techniques using different parameters accuracy, precision and recall. In this Research paper we com- pare all existing approach and find our better result for future application.
Model architecture and tile size selection for convolutional neural network training for non-small cell lung cancer detection on whole slide images
