A Deep Learning Approach to Detect and Classification of Lung Cancer

Abstract

Cancer is a name of fear to people in the world. Ev- ery year millions of people dead of cancer in the world and lung cancer is one of them. Lung cancer is classified by our research. Non-small cell lung cancer (NSCLC) is the most common of the two main types of lung cancer. Here we have classified our model NSCLC into 2 subtypes Adenocarcinoma and Squamous Cell Carcinoma and non-cancerous benign tumors. The CNN model is utilized here for classification (VGG19, ResNet50, EfficientNetB7 and MobileNetV2). We used 15 thousand image data. The Augmentor package was utilized to enhance to 15 thousand from 250 benign lung tissue, 250 lung adenocarcinomas, and 250 lung squamous cell carcinomas. In comparison to other models, ResNet50 has the best accuracy of 98% among our proposed models. By putting this model into practice, medical experts will be able to create an accurate, automatic method for diagnosing different forms of lung cancer