Szczegóły publikacji
Opis bibliograficzny
Detection of chest X-ray abnormalities using CNN based on hyperparameter optimization / Shoffan SAIFULLAH, Bambang Yuwono, Heru Cahya Rustamaji, Bayu Saputra, Felix Andika DWIYANTO, Rafał DREŻEWSKI // Engineering Proceedings [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2673-4591. — 2023 — vol. 56 iss. 1 art. no. 223, s. 1–7. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 6–7, Abstr. — Publikacja dostępna online od: 2023-11-15. — S. Saifullah - dod. afiliacja: Universitas Pembangunan Nasional Veteran Yogyakarta, Yogyakarta, Indonesia. — ASEC 2023 : 4th International Electronic Conference on Applied Sciences : 27 October–10 November 2023
Autorzy (6)
- AGHSaifullah Shoffan
- Yuwono Bambang
- Rustamaji Heru Cahya
- Saputra Bayu
- AGHDwiyanto Felix Andika
- AGHDreżewski Rafał
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150195 |
|---|---|
| Data dodania do BaDAP | 2024-01-08 |
| Tekst źródłowy | URL |
| DOI | 10.3390/ASEC2023-16260 |
| Rok publikacji | 2023 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Engineering Proceedings |
Abstract
The chest X-ray (CXR) is a commonly used diagnostic imaging test that requires significant expertise and careful observation due to the complex nature of the pathology and fine texture of lung lesions. Despite the long-term clinical training and professional guidance provided to radiologists, there is still the possibility of errors in diagnosis. Therefore, we have developed a novel approach using a convolutional neural network (CNN) model to detect the abnormalities of CXR images. The model was optimized using algorithms such as Adam and RMSprop. Also, several hyperparameters were optimized, including the pooling layer, convolutional layer, dropout layer, target size, and epochs. Hyperparameter optimization aims to improve the model’s accuracy by testing various combinations of hyperparameter values and optimization algorithms. To evaluate the model’s performance, we used scenario modeling to create 32 models and tested them using a confusion matrix. The results indicated that the best accuracy achieved by the model was 97.94%. This accuracy was based on training and test data using 4538 CXR images. The findings suggest that hyperparameter optimization can improve the CNN model’s accuracy in accurately identifying CXR abnormalities. Therefore, this study has important implications for improving the accuracy and reliability of CXR image interpretation, which could ultimately benefit patients by improving the detection and treatment of lung diseases. Acknowledging dataset constraints, we address future steps for model improvement.
