Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases

Michelle Viscaino, Matias Talamilla, Juan Cristóbal Maass, Pablo Henríquez, Paul H. Délano, Cecilia Auat Cheein, Fernando Auat Cheein*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
30 Downloads (Pure)

Abstract

Artificial intelligence-assisted otologic diagnosis has been of growing interest in the sci-entific community, where middle and external ear disorders are the most frequent diseases in daily ENT practice. There are some efforts focused on reducing medical errors and enhancing physician capabilities using conventional artificial vision systems. However, approaches with multispectral analysis have not yet been addressed. Tissues of the tympanic membrane possess optical properties that define their characteristics in specific light spectra. This work explores color wavelengths dependence in a model that classifies four middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plug. The model is constructed under a computer-aided diagnosis system that uses a convolutional neural network architecture. We trained several models using different single-channel images by taking each color wavelength separately. The results showed that a single green channel model achieves the best overall performance in terms of accuracy (92%), sensitivity (85%), specificity (95%), precision (86%), and F1-score (85%). Our findings can be a suitable alternative for artificial intelligence diagnosis systems compared to the 50% of overall misdiagnosis of a non-specialist physician.

Original languageEnglish
Article number917
JournalDiagnostics
Volume12
Issue number4
DOIs
Publication statusPublished - 7 Apr 2022

Keywords

  • artificial intelligence
  • convolutional neural network
  • deep learning
  • middle and external ear
  • otology

ASJC Scopus subject areas

  • Clinical Biochemistry

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