TY - GEN
T1 - Anemia Detection Using Convolutional Neural Network Based on Palpebral Conjunctiva Images
AU - Purwanti, Endah
AU - Amelia, Helsani
AU - Winarno,
AU - Bustomi, Muhammad Arief
AU - Yatijan, Marcella Aurelia
AU - Putri, Revita Novianti
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Anemia is a condition where the level of hemoglobin in the blood is below normal limits. Anemia can cause disruption of the oxygen transport system in the body which will affect the work of important organs such as the heart, kidneys and other organs. One of the factors causing the delay in treating anemia is that invasive procedures for examination are still considered frightening for some people. Patients with anemia, generally will experience pallor in the palpebral conjunctiva which indicates a decrease in hemoglobin levels in the blood. Palpebral conjunctival examination has the potential to be developed as a non-invasive and inexpensive alternative for anemia diagnosis. This study aims to detect anemia through image classification of the palpebral conjunctiva using a convolutional neural network (CNN). There are 3 CNN architectures used, namely AlexNet, ResNet-50 and MobileNetV2. The results showed that the performance accuracy of the AlexNet, ResNet-50 and MobileNetV2 architectures were 89.93%, 97.94%, 97.19%, respectively.
AB - Anemia is a condition where the level of hemoglobin in the blood is below normal limits. Anemia can cause disruption of the oxygen transport system in the body which will affect the work of important organs such as the heart, kidneys and other organs. One of the factors causing the delay in treating anemia is that invasive procedures for examination are still considered frightening for some people. Patients with anemia, generally will experience pallor in the palpebral conjunctiva which indicates a decrease in hemoglobin levels in the blood. Palpebral conjunctival examination has the potential to be developed as a non-invasive and inexpensive alternative for anemia diagnosis. This study aims to detect anemia through image classification of the palpebral conjunctiva using a convolutional neural network (CNN). There are 3 CNN architectures used, namely AlexNet, ResNet-50 and MobileNetV2. The results showed that the performance accuracy of the AlexNet, ResNet-50 and MobileNetV2 architectures were 89.93%, 97.94%, 97.19%, respectively.
KW - AlexNet
KW - CNN
KW - MobileNetV2
KW - Palpebral Conjunctiva
KW - ResNet-50
UR - http://www.scopus.com/inward/record.url?scp=85180369120&partnerID=8YFLogxK
U2 - 10.1109/ICTS58770.2023.10330869
DO - 10.1109/ICTS58770.2023.10330869
M3 - Conference contribution
AN - SCOPUS:85180369120
T3 - 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
SP - 117
EP - 122
BT - 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Information and Communication Technology and System, ICTS 2023
Y2 - 4 October 2023 through 5 October 2023
ER -