TY - GEN
T1 - Combining Convolutional Neural Network and Long Short-Term Memory to Classify Sinusitis
AU - Wirasati, Ilsya
AU - Rustam, Zuherman
AU - Putri Wibowo, Velery Virgina
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/11/8
Y1 - 2020/11/8
N2 - 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.
AB - 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.
KW - Classification
KW - Convolutional Neural Network
KW - Long Short-Term Memory
KW - Sinusitis
UR - http://www.scopus.com/inward/record.url?scp=85100583854&partnerID=8YFLogxK
U2 - 10.1109/DASA51403.2020.9317280
DO - 10.1109/DASA51403.2020.9317280
M3 - Conference contribution
AN - SCOPUS:85100583854
T3 - 2020 International Conference on Decision Aid Sciences and Application, DASA 2020
SP - 991
EP - 995
BT - 2020 International Conference on Decision Aid Sciences and Application, DASA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Decision Aid Sciences and Application, DASA 2020
Y2 - 7 November 2020 through 9 November 2020
ER -