An application of faster R-CNN for chest X-ray digital image classification / Mohd Taufik Rahmat
Non-digitalized chest X-ray is an effective, low-cost screening tool, and it is important to indicate pathologies. However, there are some cases of misinterpretation in the diagnostic process. Reading and interpret chest X-ray may be a simple task for a radiologist, but not every doctor can do it th...
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my-uitm-ir.645202022-09-14T03:24:08Z An application of faster R-CNN for chest X-ray digital image classification / Mohd Taufik Rahmat 2019-01 Rahmat, Mohd Taufik Imaging Non-digitalized chest X-ray is an effective, low-cost screening tool, and it is important to indicate pathologies. However, there are some cases of misinterpretation in the diagnostic process. Reading and interpret chest X-ray may be a simple task for a radiologist, but not every doctor can do it the same. This paper aims to evaluate the performance of chest X-ray image classification using Faster R-CNN architecture. To develop a chest x-ray classifier model, Tensorflow package was used with python. The results show the propose model performance accuracy is 62%. The model then was compared to random selected one medical student and general practitioner. The model shows better in term of performance to classify chest x-ray images with 62% accuracy compared to selected medical students and general practitioners with their accuracy score of 56% and 50% respectively. In term of chest X-ray interpretation in this study, the result shows that the model performance is more reliable to use for chest x-ray images classification. Tough the model performance is better, but in medical field reality, it is still far from the standard to be applied. With 62% accuracy, the model is unsafe to use. The future works are to gain more knowledge from radiologist expert to improve chest -x-ray classifier performance. 2019-01 Thesis https://ir.uitm.edu.my/id/eprint/64520/ https://ir.uitm.edu.my/id/eprint/64520/1/64520.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Ismail, Azlan (Dr) |
| institution |
Universiti Teknologi MARA |
| collection |
UiTM Institutional Repository |
| language |
English |
| advisor |
Ismail, Azlan (Dr) |
| topic |
Imaging |
| spellingShingle |
Imaging Rahmat, Mohd Taufik An application of faster R-CNN for chest X-ray digital image classification / Mohd Taufik Rahmat |
| description |
Non-digitalized chest X-ray is an effective, low-cost screening tool, and it is important to indicate pathologies. However, there are some cases of misinterpretation in the diagnostic process. Reading and interpret chest X-ray may be a simple task for a radiologist, but not every doctor can do it the same. This paper aims to evaluate the performance of chest X-ray image classification using Faster R-CNN architecture. To develop a chest x-ray classifier model, Tensorflow package was used with python. The results show the propose model performance accuracy is 62%. The model then was compared to random selected one medical student and general practitioner. The model shows better in term of performance to classify chest x-ray images with 62% accuracy compared to selected medical students and general practitioners with their accuracy score of 56% and 50% respectively. In term of chest X-ray interpretation in this study, the result shows that the model performance is more reliable to use for chest x-ray images classification. Tough the model performance is better, but in medical field reality, it is still far from the standard to be applied. With 62% accuracy, the model is unsafe to use. The future works are to gain more knowledge from radiologist expert to improve chest -x-ray classifier performance. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Rahmat, Mohd Taufik |
| author_facet |
Rahmat, Mohd Taufik |
| author_sort |
Rahmat, Mohd Taufik |
| title |
An application of faster R-CNN for chest X-ray digital image classification / Mohd Taufik Rahmat |
| title_short |
An application of faster R-CNN for chest X-ray digital image classification / Mohd Taufik Rahmat |
| title_full |
An application of faster R-CNN for chest X-ray digital image classification / Mohd Taufik Rahmat |
| title_fullStr |
An application of faster R-CNN for chest X-ray digital image classification / Mohd Taufik Rahmat |
| title_full_unstemmed |
An application of faster R-CNN for chest X-ray digital image classification / Mohd Taufik Rahmat |
| title_sort |
application of faster r-cnn for chest x-ray digital image classification / mohd taufik rahmat |
| granting_institution |
Universiti Teknologi MARA (UiTM) |
| granting_department |
Faculty of Computer and Mathematical Sciences |
| publishDate |
2019 |
| url |
https://ir.uitm.edu.my/id/eprint/64520/1/64520.pdf |
| _version_ |
1783735478555508736 |