TY - GEN
T1 - Classification of Abnormality in Chest X-Ray Images by Transfer Learning of CheXNet
AU - Almuhayar, Mawanda
AU - Lu, Henry Horng Shing
AU - Iriawan, Nur
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Deep learning development nowadays has attracted a lot of attention because of its effectiveness and good performance. The performance of deep learning in medical images analysis already can compete with medical image experts. However, there are experts that still believe deep learning only efficient for the big datasets, because of deep learning performance in small datasets still not satisfying enough. In this study, it is aimed to build a deep learning model for image classification that can achieve high accuracy using chest X-ray images with a relatively small dataset. We classify chest X-ray into a binary classification which is a normal image and image with abnormalities. We built and experimented our model using the public dataset of Shenzen Hospital dataset. We also use a different type of input based on different images preprocessing so that the model can perform accurate classification. Based on the result, pre-trained CheXNet with a newly trained fully connected network on the cropped dataset can achieve the accuracy 0.8761, the sensitivity 0.8909, and the specificity 0.8621. The performance of the model also influenced by the certain region inside the images, such as other regions outside the lung region and black colored region outside the body region.
AB - Deep learning development nowadays has attracted a lot of attention because of its effectiveness and good performance. The performance of deep learning in medical images analysis already can compete with medical image experts. However, there are experts that still believe deep learning only efficient for the big datasets, because of deep learning performance in small datasets still not satisfying enough. In this study, it is aimed to build a deep learning model for image classification that can achieve high accuracy using chest X-ray images with a relatively small dataset. We classify chest X-ray into a binary classification which is a normal image and image with abnormalities. We built and experimented our model using the public dataset of Shenzen Hospital dataset. We also use a different type of input based on different images preprocessing so that the model can perform accurate classification. Based on the result, pre-trained CheXNet with a newly trained fully connected network on the cropped dataset can achieve the accuracy 0.8761, the sensitivity 0.8909, and the specificity 0.8621. The performance of the model also influenced by the certain region inside the images, such as other regions outside the lung region and black colored region outside the body region.
KW - CheXNet
KW - abnormalities
KW - chest X-ray
KW - classification
KW - deep learning
KW - preprocessing
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85081105323&partnerID=8YFLogxK
U2 - 10.1109/ICICoS48119.2019.8982455
DO - 10.1109/ICICoS48119.2019.8982455
M3 - Conference contribution
AN - SCOPUS:85081105323
T3 - ICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences: Accelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings
BT - ICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Informatics and Computational Sciences, ICICOS 2019
Y2 - 29 October 2019 through 30 October 2019
ER -