Analisis Performa Model ResNet-50 Pada Diagnosis Pneumonia Balita Berdasarkan Citra Radiografi Thorax
One of the most serious complications of ARI is pneumonia, where this disease causes sufferers to experience pain when breathing and limited oxygen intake. According to the World Health Organization (WHO), pneumonia is classified as a life-threatening disease due to the high mortality rate caused. T...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | Indonesian |
| Published: |
LPPM Universitas Bina Sarana Informatika
2025-01-01
|
| Series: | Computer Science |
| Subjects: | |
| Online Access: | https://jurnal.bsi.ac.id/index.php/co-science/article/view/7618 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | One of the most serious complications of ARI is pneumonia, where this disease causes sufferers to experience pain when breathing and limited oxygen intake. According to the World Health Organization (WHO), pneumonia is classified as a life-threatening disease due to the high mortality rate caused. To be able to diagnose this disease, patients usually undergo various medical examination methods, one of which is through chest radiography. However, the challenge in diagnosing pneumonia generally lies in the complexity and uncertainty in interpreting the results of these methods. Therefore, this study was conducted with the aim of building an image classification model based on the Chest radiography dataset from toddler patients using the ResNet-50 architecture, which is a variant of the Convolutional Neural Networks (CNN) algorithm. The combination of the two methods is applied to analyze and process images and obtain pattern recognition with high accuracy. The research methods used include the application of data augmentation, CNN architecture design, model training, and performance evaluation. The evaluation results show that the model has quite good performance with an accuracy of 85%, which indicates the model's ability to classify images with a fairly high level of accuracy, and has the potential to help the pneumonia diagnosis process more efficiently and accurately. |
|---|---|
| ISSN: | 2808-9065 2774-9711 |