Lung Cancer Detection Using VGG16 and CNN

Abstract

Globally, lung cancer constitutes a significant contributor to cancer-related complications, with early identification playing a critical role in enhancing patient prognosis. In recent times, machine learning approaches, particularly convolutional neural networks (CNNs), have demonstrated notable success in automating lung cancer detection using medical diagnostic tools. This study aims to explore the potential of a pre-trained VGG16 and CNN model in detecting lung cancer through the analysis of histopathological images. We utilize openly accessible dataset containing lung tissue histopathological images for training and assessing our model. Our evaluation reveals that the VGG16 CNN model effectively discerns cancerous conditions in lung tissue images with remarkable precision.