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

E. Kurniawan, P. Prajitno, D. S. Soejoko

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number012018
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 15 Jun 2020
Event3rd Annual Scientific Meeting on Medical Physics and Biophysics, PIT-FMB in conjunction with the 17th South-East Asia Congress of Medical Physics, SEACOMP 2019 - Bali, Indonesia
Duration: 8 Aug 201910 Aug 2019


  • computer-aided detection
  • CT image
  • deep learning
  • mediastinal lymph nodes
  • simple architectural convolutional neural network


Dive into the research topics of 'Computer-Aided Detection of Mediastinal Lymph Nodes using Simple Architectural Convolutional Neural Network'. Together they form a unique fingerprint.

Cite this