Comparison of Deep Learning-Based Recognition Techniques for Medical and Biomedical Images

Tomas Majtner*, Esmaeil S. Nadimi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

The recognition and classification of medical and biomedical images typically suffer from the problem of a low number of annotated samples. This comes along with the problem of efficient training of the current deep learning frameworks. Therefore, many researchers opt for various techniques which could substitute the traditional training of convolutional neural networks (CNN) from scratch. In this article, we are comparing multiple of these methods, including transfer learning and using the CNNs as feature extractors. The paper contains results on two datasets with different modalities and three different CNN architectures. We demonstrate the high effectiveness of transfer learning and suggest that, in some cases, it is worth retraining more layers at the end of the network for achieving higher accuracy.
Original languageEnglish
Title of host publicationInternational Conference on Computer Analysis of Images and Patterns : CAIP 2019: Computer Analysis of Images and Patterns
EditorsMario Vento, Gennaro Percannella
PublisherSpringer
Publication date22. Aug 2019
Pages492-504
ISBN (Print)978-3-030-29887-6
ISBN (Electronic)978-3-030-29888-3
DOIs
Publication statusPublished - 22. Aug 2019
EventThe 18th International Conference on Computer Analysis of Images and Patterns -
Duration: 2. Sept 20196. Sept 2019
https://caip2019.unisa.it/

Conference

ConferenceThe 18th International Conference on Computer Analysis of Images and Patterns
Period02/09/201906/09/2019
Internet address
SeriesLecture Notes in Computer Science
Volume11678
ISSN0302-9743

Keywords

  • Image recognition
  • GoogLeNet
  • VGG-16
  • ResNet-50
  • Transfer learning
  • Polyp detection
  • HEp-2 image classification

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