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Suzuki, K., Armato, S.G., Li, F., Sone, S. and Doi, K. (2003) Massive Training Artificial Neural Network (MTANN) for Reduction of False Positives in Computerized Detection of Lung Nodules in Low-Dose Computed Tomography. The International Journal of Medical Physics Research and Practice, 30, 1602-1617.
https://doi.org/10.1118/1.1580485

has been cited by the following article:

• TITLE: Lung Cancer Detection Using CT Image Based on 3D Convolutional Neural Network

  AUTHORS: Tasnim Ahmed, Mst. Shahnaj Parvin, Mohammad Reduanul Haque, Mohammad Shorif Uddin

  KEYWORDS: Lung Cancer, Convolutional Neural Network, Tensorflow, CT Scan

  JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.3, March 5, 2020

  ABSTRACT: Early detection of lung nodule is of great importance for the successful diagnosis and treatment of lung cancer. Many researchers have tried with diverse methods, such as thresholding, computer-aided diagnosis system, pattern recognition technique, backpropagation algorithm, etc. Recently, convolutional neural network (CNN) finds promising applications in many areas. In this research, we investigated 3D CNN to detect early lung cancer using LUNA 16 dataset. At first, we preprocessed raw image using thresholding technique. Then we used Vanilla 3D CNN classifier to determine whether the image is cancerous or non-cancerous. The experimental results show that the proposed method can achieve a detection accuracy of about 80% and it is a satisfactory performance compared to the existing technique.