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Lung Nodule Diagnosis via Deep Learning and Swarm Intelligence

  • Cesar Affonso de Pinho Pinheiro
  • Nadia NedjahEmail author
  • Luiza de Macedo Mourelle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

Abstract

In general, it is difficult to perform cancer diagnosis. In particular, pulmonary cancer is one of the most aggressive type of cancer and hard to be detected. When properly identified in its early stages, the chances of survival of the patient increase significantly. However, this detection is a hard problem, since it depends only on visual inspection of tomography images. Computer aided diagnosis methods can improve a great deal the detection and, thus, increasing the surviving rates. In this paper, we exploit computational intelligence techniques, such as deep learning, convolutional neural networks and swarm intelligence, in order to propose an efficient approach that allows identifying carcinogenic nodules in digital tomography scans. We use 7 different swarm intelligence techniques to approach the learning stage of a convolutional deep learning network. We are able to identify and classify cancerous pulmonary nodules successfully in the tomography scans of the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed approach, which consists of training Convolutional Neural Networks using swarm intelligence techniques, proved to be more efficient than the classic training with Back-propagation and Gradient Descent. It improves the average accuracy from 93% to 94%, precision from 92% to 94%, sensitivity from 91% to 93% and specificity from 97% to 98%.

Keywords

Deep learning Swarm intelligence Lung cancer detection Convolutional neural networks 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Cesar Affonso de Pinho Pinheiro
    • 1
  • Nadia Nedjah
    • 1
    Email author
  • Luiza de Macedo Mourelle
    • 2
  1. 1.Department of Electronics Engineering and Telecommunications, Faculty of EngineeringState University of Rio de JaneiroRio de JaneiroBrazil
  2. 2.Department of Systems Engineering and Computation, Faculty of EngineeringState University of Rio de JaneiroRio de JaneiroBrazil

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