Combining Convolutional Neural Network and Long Short-Term Memory to Classify Sinusitis

Ilsya Wirasati, Zuherman Rustam, Velery Virgina Putri Wibowo

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

Abstract

As one of the common health problems, sinusitis is inflammation of the mucous membranes lining one or more of the paranasal sinuses. Improvement of detection tools to classify acute or chronic sinusitis is required because of its impact on the patient's treatment. In some of the previous research, deep learning has demonstrated good accuracy to classify disease. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are now popularly used for deep learning tasks. This research applied one dimensional (1D) CNN and its advanced modification with LSTM called 1D CNN-LSTM to classify the type of sinusitis. Data sinusitis patients are received from Cipto Mangunkusumo Hospital, Jakarta, Indonesia. This dataset consists of 200 data with four features, such as Gender, Age, Hounsfield Unit (HU), and Air Cavity. The result is 1D CNN-LSTM has higher accuracy than 1D CNN with 98,33% of accuracy.

Original languageEnglish
Title of host publication2020 International Conference on Decision Aid Sciences and Application, DASA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages991-995
Number of pages5
ISBN (Electronic)9781728196770
DOIs
Publication statusPublished - 8 Nov 2020
Event2020 International Conference on Decision Aid Sciences and Application, DASA 2020 - Virtual, Sakheer, Bahrain
Duration: 7 Nov 20209 Nov 2020

Publication series

Name2020 International Conference on Decision Aid Sciences and Application, DASA 2020

Conference

Conference2020 International Conference on Decision Aid Sciences and Application, DASA 2020
CountryBahrain
CityVirtual, Sakheer
Period7/11/209/11/20

Keywords

  • Classification
  • Convolutional Neural Network
  • Long Short-Term Memory
  • Sinusitis

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