Employing U-NET and Rotational Based Convolutional Neural Network to Build an Automatic Lung Cancer Segmentation and Classification System

Abstract

The most common cancer-related cause of death globally is lung cancer. The key to effective lung cancer treatment and higher survival rates is early diagnosis. Converting a radiologist's diagnosing procedure to computer assisted results in more accurate results and an earlier diagnosis. The difficulty is that building an effective model for segmentation and classification. In this paper, we suggest a system for detecting lung cancer that makes use of a number of methods for precise and effective diagnosis. To enhance picture quality, our method pre-processes CT scan images using a Gaussian filter and contrast stretching. For the purpose of determining the borders of lung nodules with high precision, the U-Net architecture with the Adam optimizer is used. Then, a Gaussian mixture model (GMM) with EM optimisation and pixel padding is used to extract features. The rotational-based Convolutional Neural Network (RBCNN) classifier successfully categorises the nodules as benign and malignant using these form variables as inputs