Segmentation and Classification of Lung Cancer using Deep Learning Techniques

Abstract

To increase the survival rates for lung cancer, early identification and diagnosis are essential. The detection becomes crucial for a number of reasons, including visual similarity, heterogeneity, and low contrast variation. The several varieties of lung cancer that can be categorised histologically are determined by the kind of cells a pathologist can detect under a microscope. 15% of lung cancer cases are caused by small cell lung cancer (SCLC), while 85% of cases are caused by non-small cell lung cancer (NSCLC). The three main types of NSCLC are lung adenocarcinoma, big cell carcinoma, and squamous cell carcinoma (SQCC). Deep learning models provides high level services to the healthcare domain. The proposed research focuses on applying deep learning models to segment and classify images of lung cancer histopathology. Images are segmented using the U-Net model. In order to categorise the different forms of lung cancer histopathology images, classification models like ResNet-50, VGG-16, and EfficientNet-B5 and Ensemble models of these three models were utilised. For the purpose of evaluating all the classifiers, performance metrics like accuracy, precision, recall, and F1 Score were computed. The U-Net model achieves 81% accuracy. ResNet-50 achieved an accuracy of 91%, VGG-16 94%, EfficientNet-B5 97%, and Ensemble model 99%, respectively. Among all models, an ensemble model is the most accurate.