Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography

• Published in: Multidimensional systems and signal processing Vol. 31; no. 3; pp. 1163 - 1183
• Main Authors: Wang, Yi, Zhang, Hao, Chae, Kum Ju, Choi, Younhee, Jin, Gong Yong, Ko, Seok-Bum
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2020; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Cancer; Circuits and Systems; Classification; Computed tomography; Computer architecture; Diagnostic systems; Electrical Engineering; Engineering; Feature extraction; Multiscale analysis; Neural networks; Nodules; Signal,Image and Speech Processing; Tomography; Deep learning; Lung nodule classification; Computer tomography; Convolutional neural network
• ISSN: 0923-6082; 1573-0824
• DOI: 10.1007/s11045-020-00703-6

Computed tomography (CT) is widely used to locate pulmonary nodules for preliminary diagnosis of the lung cancer. However, due to high visual similarities between malignant (cancer) and benign (non-cancer) nodules, distinguishing malignant from malign nodules is not an easy task for a thoracic radiologist. In this paper, a novel convolutional neural network (ConvNet) architecture is proposed to classify the pulmonary nodules as either benign or malignant. Due to the high variance of nodule characteristics in CT scans, such as size and shape, a multi-path, multi-scale architecture is proposed and applied in the proposed ConvNet to improve the classification performance. The multi-scale method utilizes filters with different sizes to more effectively extracted nodule features from local regions, and the multi-path architecture combines features extracted from different ConvNet layers thereby enhancing the nodule features with respect to global regions. The proposed ConvNet is trained and evaluated on the LUNGx Challenge database, and achieves a sensitivity of 0.887 and a specificity of 0.924 with an area under the curve (AUC) of 0.948. The proposed ConvNet achieves a 14% AUC improvement compared to the state-of-the-art unsupervised learning approach. The proposed ConvNet also outperforms the other state-of-the-art ConvNets explicitly designed for pulmonary nodule classification. For clinical usage, the proposed ConvNet could potentially assist the radiologists to make diagnostic decisions in CT screening.