Evaluation of deep feedforward neural networks for classification of diffuse lung diseases

Isadora Cardoso, Eliana Almeida, HÉCTOR GABRIEL ALLENDE CID, Alejandro C. Frery, Rangaraj M. Rangayyan, Paulo M. Azevedo-Marques, Heitor S. Ramos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Diffuse Lung Diseases (DLDs) are a challenge for physicians due their wide variety. Computer-Aided Diagnosis (CAD) are systems able to help physicians in their diagnoses combining information provided by experts with Machine Learning (ML) methods. Among ML techniques, Deep Learning has recently established itself as one of the preferred methods with state-of-the-art performance in several fields. In this paper, we analyze the discriminatory power of Deep Feedforward Neural Networks (DFNN) when applied to DLDs. We classify six radiographic patterns related with DLDs: pulmonary consolidation, emphysematous areas, septal thickening, honeycomb, ground-glass opacities, and normal lung tissues. We analyze DFNN and other ML methods to compare their performance. The obtained results show the high performance obtained by DFNN method, with an overall accuracy of 99.60%, about 10% higher than the other studied ML methods.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings
EditorsSergio Velastin, Marcelo Mendoza
PublisherSpringer Verlag
Pages152-159
Number of pages8
ISBN (Print)9783319751924
DOIs
StatePublished - 2018
Externally publishedYes
Event22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017 - Valparaiso, Chile
Duration: 7 Nov 201710 Nov 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10657 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017
CountryChile
CityValparaiso
Period7/11/1710/11/17

Keywords

  • Computer-aided diagnosis
  • Deep feedforward neural network
  • Deep learning
  • Diffuse lung diseases
  • Machine learning

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