Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

Cheng-Jian Lin,Shiou-Yun Jeng,Mei-Kuei Chen

doi:10.3390/app10072591

Abstract

Lung cancer is one of the common causes of cancer deaths. Early detection and treatment of lung cancer is essential. However, the detection of lung cancer in patients produces many false positives. Therefore, increasing the accuracy of the classification of diagnosis or true detection by computed tomography (CT) is a difficult task. Solving this problem using intelligent and automated methods has become a hot research topic in recent years. Hence, we propose a 2D convolutional neural network (2D CNN) with Taguchi parametric optimization for automatically recognizing lung cancer from CT images. In the Taguchi method, 36 experiments and 8 control factors of mixed levels were selected to determine the optimum parameters of the 2D CNN architecture and improve the classification accuracy of lung cancer. The experimental results show that the average classification accuracy of the 2D CNN with Taguchi parameter optimization and the original 2D CNN in lung cancer recognition are 91.97% and 98.83% on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, and 94.68% and 99.97% on the International Society for Optics and Photonics with the support of the American Association of Physicists in Medicine (SPIE-AAPM) dataset, respectively. The proposed method is 6.86% and 5.29% more accurate than the original 2D CNN on the two datasets, respectively, proving the superiority of proposed model.

Highlights

Lung cancer is one of the critical diseases with rapidly rising morbidity and mortality rates, being the primary reason for cancer-related mortality around the world, with 1.8 million deaths annually [1]
For the the purpose purpose of of overcoming the challenges associated with with improving improving the the classification classification performance of benign and malignant tumors from images, we propose a performance of benign and malignant tumors from computed tomography (CT) images, we propose a 2D convolutional neural network (2D convolutional neural networks (CNNs)) with with Taguchi
We compared the performance of the proposed 2D CNN with Taguchi parametric optimization with the original 2D CNN model on the LIDC-IDRI and SPIE-AAPM datasets

Summary

Introduction

Lung cancer is one of the critical diseases with rapidly rising morbidity and mortality rates, being the primary reason for cancer-related mortality around the world, with 1.8 million deaths annually [1]. The early detection rate has increased significantly, distinguishing malignant from benign tumors is one of the most challenging tasks [2,3]. Early and accurate diagnosis of lung cancer is essential. Many researchers applied machine learning models to detect and diagnose lung computed tomography (CT) images with the help of various computer assisted detection (CAD) systems, such as convolutional neural networks (CNNs), which demonstrated classification performance on medical images [4,5]. CNN for lung nodule type classification from CT images. A CNN was integrated to assess pulmonary nodule malignancy in an automated existing pipeline of lung cancer detection [7]

Methods

Discussion

Conclusion