Computer-Aided Detection of Mediastinal Lymph Nodes using Simple Architectural Convolutional Neural Network

• Published in: Journal of physics. Conference series 2020-03, Vol.1505 (1), p.12018
• Main Authors: Kurniawan, E, Prajitno, P, Soejoko, D S
• Format: Article
• Language: English
• Subjects: Architecture; Artificial neural networks; Complexity; Computed tomography; computer-aided detection; CT image; deep learning; Enlargement; Lung cancer; mediastinal lymph nodes; Neural networks; Physics; simple architectural convolutional neural network
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1505/1/012018

Lung cancer is the most common and the deadliest cancer in the world. Lung cancer staging usually was done by radiologist by detecting mediastinal lymph node (LN) enlargement. Mediastinal LN is difficult to be detected visually due to its low contrast to the surrounding tissues, various size and shape, and sparse location. Therefore, computer-aided detection (CADe) system has been developed as a tool for radiologist to detect mediastinal LN automatically. The state of the art mediastinal LN CADe system use complex architectural convolutional neural network (CNN). However, more simple architecture of the CNN is needed to reduce the computational complexity of the CADe system, especially if the system was intended to be used in a regular computer. Therefore, in this experiment we used simple architectural 2D CNN which is converted to fully convolutional network (FCN) to detect mediastinal LN candidate in a stack of CT images. Then, the mediastinal LN candidates were classified using 3D CNN to reduce the false positive (FP). The best performance of this CADe system was 65% of sensitivity at 5 FP/patient.