Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification
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ORIGINAL PAPER
Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification Hong Liu 1 & Haichao Cao 1 & Enmin Song 1 Chih-Cheng Hung 2
&
Guangzhi Ma 1 & Xiangyang Xu 1 & Renchao Jin 1 & Chuhua Liu 1 &
# Society for Imaging Informatics in Medicine 2020
Abstract Classification of benign and malignant in lung nodules using chest CT images is a key step in the diagnosis of early-stage lung cancer, as well as an effective way to improve the patients’ survival rate. However, due to the diversity of lung nodules and the visual similarity of lung nodules to their surrounding tissues, it is difficult to construct a robust classification model with conventional deep learning–based diagnostic methods. To address this problem, we propose a multi-model ensemble learning architecture based on 3D convolutional neural network (MMEL-3DCNN). This approach incorporates three key ideas: (1) Constructed multi-model network architecture can be well adapted to the heterogeneity of lung nodules. (2) The input that concatenated of the intensity image corresponding to the nodule mask, the original image, and the enhanced image corresponding to which can help training model to extract advanced feature with more discriminative capacity. (3) Select the corresponding model to different nodule size dynamically for prediction, which can improve the generalization ability of the model effectively. In addition, ensemble learning is applied in this paper to further improve the robustness of the nodule classification model. The proposed method has been experimentally verified on the public dataset, LIDC-IDRI. The experimental results show that the proposed MMEL-3DCNN architecture can obtain satisfactory classification results. Keywords Benign and malignant classification . Computer-aided diagnosis . Image enhancement . Multi-model ensemble architecture . 3D CNN
Introduction According to global cancer statistics in 2018, lung cancer, prostate cancer, breast cancer, and colorectal cancer are the most common causes of cancer death in humans, and they account for 45% of all cancer deaths, 25% of which are caused by lung cancer (5-year survival rate is 18%) [1]. However, if lung cancer can be diagnosed early, the patient’s 5-year survival rate can be tripled [2]. Early lung cancer diagnosis using lung-based computed tomography (CT) images is an important strategy to improve patient survival [3]. In particular, the use of deep learning–based methods in CT images to classify * Enmin Song [email protected] 1
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2
Laboratory for Machine Vision and Security Research, Kennesaw State University, Kennesaw, GA, USA
the benign and malignant lung nodules is a valuable task. Because it can help doctors to judge the benign and malignant of early lung nodules, to reduce the risk of misdiagnosis and missed diagnosis, which is one of the essential tasks in the early detection of lung cancer [4, 5]. T
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