Lung-Deep: A Computerized Tool for Detection of Lung Nodule Patterns using Deep Learning Algorithms Detection of Lung Nodules Patterns

The detection of lung-related disease for radiologists is a tedious and time-consuming task. For this reason, automatic computer-aided diagnosis (CADs) systems were developed by using digital CT scan images of lungs. The detection of lung nodule patterns is an important step for the automatic development of CAD system. Currently, the patterns of lung nodule are detected through domain-expert knowledge of image processing and accuracy is also not up-to-the-mark. Therefore, a computerized CADs tool is presented in this paper to identify six different patterns of lung nodules based on multi-layer deep learning ( known as Lung-Deep) algorithms compare to state-of-the-art systems without using the technical image processing methods. A multilayer combination of the convolutional neural network (CNN), recurrent neural networks (RNNs) and softmax linear classifiers are integrated to develop the Lung-Deep without doing any pre- or post-processing steps. The Lung-Deep system is tested with manually draw radiologist contours on the 1200 images including 3250 nodules by using statistical measures. On this dataset, the higher sensitivity (SE) of 88%, specificity (SP) of 80% and 0.98 of the area under the receiver operating curve (AUC) of 0.98 are obtained compared to other systems. Hence, this proposed lung-deep system is outperformed by integrating different layers of deep learning algorithms to detect six patterns of nodules.

@article{Abbas2017,
  title     = {Lung-Deep: A Computerized Tool for Detection of Lung Nodule Patterns using Deep Learning Algorithms Detection of Lung Nodules Patterns},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {10},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Qaisar Abbas},
  doi       = {10.14569/IJACSA.2017.081015},
  url       = {https://doi.org/10.14569/IJACSA.2017.081015}
}