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Ongoing Work on Deep Learning for Lung Cancer Prediction

  • Oier Echaniz
  • Manuel GrañaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)

Abstract

Deep learning is one of the breakthrough technologies that have emergent in the last few years. It has been applied to a wide variety of problems, most of them related with image processing. It is also being considered for 3D data in medical image processing. This paper is a report of ongoing work about the development of deep learning architectures for lung cancer prediction. Data has been extracted from an ongoing Kaggel challenge, involving multi-center CTA data. First we have normalized in intensity the images. Then we have devised an auto encoder architecture with convolutional layers to obtain a compressed representation of the lung images. These representations are fed as features to a random forest classifier.

Keywords

Graphic Processing Unit Deep Learning Convolutional Neural Network Minority Class Imbalanced Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Grupo de Inteligencia Computacional (GIC)Universidad Del País Vasco (UPV/EHU)San SebastiánSpain

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