Deep Learning-based Lung Cancer Classification of CT Images using Augmented Convolutional Neural Networks

Abstract

Lung cancer is worldwide the second death cancer, both in prevalence and lethality, both for women and men. The applicability of machine learning and pattern classification in lung cancer detection and classification is proposed. Pattern classification algorithms can classify the input data into different classes underlying the characteristic features in the input. Early identification of lung cancer using pattern recognition can save lives by analyzing the significant number of Computed Tomography images. Convolutional Neural Networks recently achieved remarkable results in various applications including Lung cancer detection in Deep Learning. The deployment of augmentation to improve the accuracy of a Convolutional Neural Network has been proposed. Data augmentation is utilized to find suitable training samples from existing training sets by employing various transformations such as scaling, rotation, and contrast modification. The LIDC-IDRI database is utilized to assess the networks. The proposed work showed an overall accuracy of 95%. Precision, recall, and F1 score for benign test data are 0.93, 0.96, and 0.95, respectively, and 0.96, 0.93, and 0.95 for malignant test data. The proposed system has impressive results when compared to other state-of-the-art approaches.