Performance of VGG-16 Convolutional Neural Network Model Based Lung Cancer Classification on Computed Tomography

Performance of VGG-16 Convolutional Neural Network Model Based Lung Cancer Classification on Computed Tomography

Dr. Amjad Khan

Associate Professor Dept. of Master of Computer Applications

Bapuji Institute of Engineering & Technology, Davanagere, Karnataka, India

Affiliated to Visvesvaraya Technological University Belagavi, Karnataka, India

Abstract— The robust nodule detection challenge in lung cancer identification has become difficult due to the variability of lung nodules and the complexity of the surrounding environment. Early detection of lung nodules is crucial for lung cancer survival and is an effective strategy to reduce patient mortality. The proposed method for identifying lung nodules from CT images utilizes VGG-16 convolutional neural networks, eliminating the need for manual feature extraction, as per previous feedback. The network is fed with raw lung CT images from publicly available LIDC-IDRI dataset. The VGG-16 convolutional neural network successfully classified lung CT images into benign and malignant categories, achieving 86% accuracy and reducing false positive rates.

Keywords— VGG-16, Lung Cancer, Computed Tomography, Classification