Lung CT Imaging Sign Classification through Deep Learning on Small Data

TL;DR

This paper presents a deep learning approach using GAN-generated data to classify lung imaging signs in CT scans, achieving high accuracy on small datasets and demonstrating effectiveness in medical image classification.

Contribution

The study introduces a novel two-stage training process combining GAN-generated data with real data to improve classification accuracy on small medical datasets.

Findings

Pre-trained CNN achieves 88.4% accuracy with GAN data.

Fine-tuning increases accuracy to 95.0%.

Achieves state-of-the-art results on the LISS database.

Abstract

The annotated medical images are usually expensive to be collected. This paper proposes a deep learning method on small data to classify Common Imaging Signs of Lung diseases (CISL) in computed tomography (CT) images. We explore both the real data and the data generated by Generative Adversarial Network (GAN) to improve the reliability and the generalization of learning. First, we use GAN to generate a large number of CISLs from small annotated data, which are difficult to be distinguished from real counterparts. These generated samples are used to pre-train a Convolutional Neural Network (CNN) for classifying CISLs. Second, we fine-tune the CNN classification model with real data. Experiments were conducted on the LISS database of CISLs. We successfully convinced radiologists that our generated CISLs samples were real for 56.7% of our experiments. The pre-trained CNN model achieves 88.4% of mean accuracy of binary classification, and after fine-tuning, the mean accuracy is significantly increased to 95.0%. For multi-classification of all types of CISLs and normal tissues, through the two stages of training, the mean accuracy, sensitivity and specificity are up to about 91.83%, 92.73% and 99.0%, respectively. To our knowledge, this is the best result achieved on the LISS database, which demonstrates that the proposed method is effective and promising for fulfilling deep learning on small data.