Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification

Chun-Hui Lin,Yu-Chi Li,Cheng-Jian Lin,Shyh-Hau Wang

doi:10.3390/app11020480

Abstract

Cancer is the leading cause of death worldwide. Lung cancer, especially, caused the most death in 2018 according to the World Health Organization. Early diagnosis and treatment can considerably reduce mortality. To provide an efficient diagnosis, deep learning is overtaking conventional machine learning techniques and is increasingly being used in computer-aided design systems. However, a sparse medical data set and network parameter tuning process cause network training difficulty and cost longer experimental time. In the present study, the generative adversarial network was proposed to generate computed tomography images of lung tumors for alleviating the problem of sparse data. Furthermore, a parameter optimization method was proposed not only to improve the accuracy of lung tumor classification, but also reduce the experimental time. The experimental results revealed that the average accuracy can reach 99.86% after image augmentation and parameter optimization.

Highlights

According to a report from the World Health Organization in 2018, there were about9.6 million deaths from cancer globally, of which 1.76 million cases were attributed to lung cancers [1]
To reduce the workload of analyzing computed tomography (CT) images manually and to avoid subjective interpretations, machine learning techniques are applied to computer-aided design systems for objectively auxiliary diagnosis
An accurate lung tumor classification is a crucial role for early diagnosis

Summary

Introduction

According to a report from the World Health Organization in 2018, there were about9.6 million deaths from cancer globally, of which 1.76 million cases were attributed to lung cancers [1]. Studies have identified environmental factors and smoking as major causes of lung cancer [2]. Chest X-ray, computed tomography (CT) and magnetic resonance imaging are modalities used to evaluate lung cancer [3,4]. The chest X-ray is the first test in diagnosing lung cancer. It indicates abnormal formations in the lungs. Compared to a chest X-ray, a CT scan can show a more detailed view of the lungs and can show the exact shape, size, and location of formations. A CT scan is a major diagnostic tool for the assessment of lung cancer. The studies of CNNs have been continually innovating and improving

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Results

Conclusion