A generalized framework for lung Cancer classification based on deep generative models
Wessam M. Salama, Ahmed Shokry, Moustafa H. Aly
Abstract
Abstract A new generalized framework for lung cancer detection and classification are introduced in this paper. Specifically, two types of deep models are presented. The first model is a generative model to capture the distribution of the important features in a set of small class-unbalanced collected CXR images. This generative model can be utilized to synthesize any number of CXR images for each class. For example, our generative model can generate images with tumors with different sizes and positions in the lung. Hence, the system can automatically convert the small unbalanced collected dataset to a larger balanced one. The second model is the ResNet50 that is trained using the large balanced dataset for cancer classification into benign and malignant. The proposed framework acquires 98.91% overall detection accuracy, 98.85% area under curve (AUC), 98.46% sensitivity, 97.72% precision, 97.89% F1 score. The classifier takes 1.2334 s on average to classify a single image using a machine with 13GB RAM.