APPLICATION OF COST-SENSITIVE CONVOLUTIONAL NEURAL NETWORK FOR PNEUMONIA DETECTION
AbstractPneumonia is a disease caused by a viral, bacterial, or fungal infection. In the diagnostic process of pneumonia, one approach is to use X-ray images. One of the existing problems is the lack of qualified and experienced medical personnel to recognize the X-ray images that have been taken. For this reason, an alternative is needed to detect pneumonia. Existing research shows that the use of convolutional neural networks can effectively detect pneumonia X-ray images. However, one of the problems is that this approach focuses a lot on accuracy without considering performance criteria such as sensitivity and specificity. To solve this problem, a cost-sensitive based approach has been proposed. In this study, a convolutional neural network-based model was created and trained using a cost-sensitive and non-cost sensitive approach. From the results obtained, it is seen that the model made still has a comparatively low level of accuracy. However, it is found that training with a cost-sensitive approach is able to improve performance on the specificity side, although at the expense of performance on the sensitivity side.
Download data is not yet available.
How to Cite
ANANTAMA, Rizki. APPLICATION OF COST-SENSITIVE CONVOLUTIONAL NEURAL NETWORK FOR PNEUMONIA DETECTION. Jurnal Ilmiah Kursor, [S.l.], v. 11, n. 3, p. 101, july 2022. ISSN 2301-6914. Available at: <https://kursorjournal.org/index.php/kursor/article/view/264>. Date accessed: 12 aug. 2022. doi: https://doi.org/10.21107/kursor.v11i3.264.
This work is licensed under a Creative Commons Attribution 4.0 International License.