Classification of Computed Tomography Images with Pleural Effusion Disease Using Convolutional Neural Networks

David Benavente, Gustavo Gatica, Ivan Derpich

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

Abstract

On the present work we use two different convolutional neural nets architectures for the classification of computed tomography images with pleural effusion disease. We decided to use the convolutional neural networks due to the great advances achieved by this kind of nets in image classification problems. We work with a real-world data anonymized and provided by an Imagenology Department of public hospital from Chile. The data was classified by medics of the hospital. Due to the limitations on graphics resource, we decided training the algorithms from scratch, avoiding overfitting with regularization techniques and optimizing the training process programming callbacks. For testing, we used a set of 1,000 images and evaluate with classification metrics like True positive rate, True negative rate and Accuracy. Results achieved were not optimal due to overfitting of algorithms. For future works, we will use other architectures of convolutional neural networks and with Transfer learning technique on the architectures.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2021 Intelligent Systems Conference, IntelliSys
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Pages559-565
Number of pages7
ISBN (Print)9783030821982
DOIs
Publication statusPublished - 2022
Event Intelligent Systems Conference, IntelliSys 2021 - Virtual, Online
Duration: 1 Sep 20212 Sep 2021

Publication series

NameLecture Notes in Networks and Systems
Volume296
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference Intelligent Systems Conference, IntelliSys 2021
CityVirtual, Online
Period1/09/212/09/21

Keywords

  • Computed tomography
  • Deep learning
  • Medical imaging

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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