# Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images
## 1 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]. Although the early detection rate has increased significantly, distinguishing malignant from benign tumors is one of the most challenging tasks [2, 3]. Therefore, early and accurate diagnosis of lung cancer is essential. In recent years, 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]. [PERSON] et al. [6] proposed a multi-view multi-scale CNN for lung nodule type classification from CT images. In another study, a CNN was integrated to assess pulmonary nodule malignancy in an automated existing pipeline of lung cancer detection [7]. However, this method is restricted to a limited number of visual parameters and the fixed value range. Thus, increasing the accuracy of lung cancer classification through CNN with parametric optimization will play a vital role in lung cancer recognition from CT images.  
CT is widely used in screening procedures to reduce high mortality and to obtain many details about lung nodules and the surrounding structures. To effectively detect lung cancer from CT images, CNNs have demonstrated outstanding performance in applications including vision and images inthe medical field in CAD systems [8]. A CNN model was used for classification, including three convolutional layers, three pooling layers, and two fully-connected layers [9]. Two-dimensional (2D) CNN has been applied in various fields with significant results such as image classification, face recognition, and natural language processing [10; 11]. The design of the existing CAD requires the training of a large number of parameters, but parameters setting is complicated, so the parameters must be optimized to increase the accuracy of the classification [12]. [PERSON] and [PERSON] [13] applied the Taguchi method to evaluate the output quality characteristics to predict the optimum parameters. In this study, we used the Taguchi method to find optimal parameter combinations based on the design of experiments and to solve the complicated parameters setting problem in CAD systems.  
The Taguchi method is widely used for statistical analysis, and has been effectively used to help design experiments and obtain highly reliable results in studies. To optimize factors that affect the performance characteristics of a process or system, the Taguchi method reduces time and material costs [14]. The 2D CNN contains three convolutional, three max-pooling, and two fully-connected layers, and has the same deep learning architecture, but using a 2D CNN single view to train a large number of parameters might produce unstable predictions [15]. To alleviate this problem, we used a 2D CNN with the Taguchi method to find optimal parameter combinations.  
For the purpose of overcoming the challenges associated with improving the classification performance of benign and malignant tumors from CT images, we propose a 2D CNN with Taguchi parametric optimization to address the complicated parameters setting problem and to increase the accuracy of classification. The objective of this study was to use the 2D CNN with Taguchi parametric optimization to determine the optimum parameters of the 2D CNN architecture and improve the lung cancer classification accuracy. The contribution is finding the optimal parameter combinations in the 2D CNN architecture on proven datasets to improve accuracy for lung cancer recognition with the help of various CAD systems.  
The remainder of this is study is organized as follows. Section 2 presents the proposed method including the 2D CNN and the experiments in the Taguchi method. The experimental results are reported in Section 3. Finally, the conclusion is provided in Section 4.