Pulmonary fibrosis progression prediction based on ResNet50 architecture
Pulmonary fibrosis (PF) is a chronic lung disease which causes permanent scarring of lung tissue over time. Current treatment options focus on slowing down the scarring process and increasing quality of life for the patient. However, due to the variable rate of progression between patients, and the...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166528 |
| _version_ | 1811697064150564864 |
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| author | Wu, Jingyuan |
| author2 | Jagath C Rajapakse |
| author_facet | Jagath C Rajapakse Wu, Jingyuan |
| author_sort | Wu, Jingyuan |
| collection | NTU |
| description | Pulmonary fibrosis (PF) is a chronic lung disease which causes permanent scarring of lung tissue over time. Current treatment options focus on slowing down the scarring process and increasing quality of life for the patient. However, due to the variable rate of progression between patients, and the potential lack of visual signs, there is still a need to develop new methods of assessing CT scans in order to obtain more accurate prediction results. This paper evaluates a convolutional neural network (CNN)-based method to predict the disease progression using the path of Forced Vital Capacity (FVC) decline, with the hopes that it could better aid doctors in managing the care of patients suffering from PF. The proposed model uses a ResNet50 backbone to predict a linear rate of FVC decline from inputs consisting of a baseline CT scan, and tabular patient data. The predicted gradient will then be used to calculate the predicted FVC value on a weekly basis. The model is trained and evaluated on a dataset obtained from the OSIC Pulmonary Fibrosis Progression challenge, which also served as the benchmark for the evaluation of the proposed model. The proposed model was not able to obtain any improvement over the state-of-the-art solutions, and changes to the current models are suggested for future improvement. |
| first_indexed | 2024-10-01T07:49:19Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166528 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:49:19Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1665282023-05-12T15:36:51Z Pulmonary fibrosis progression prediction based on ResNet50 architecture Wu, Jingyuan Jagath C Rajapakse School of Computer Science and Engineering ASJagath@ntu.edu.sg Engineering::Computer science and engineering::Computer applications::Life and medical sciences Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Pulmonary fibrosis (PF) is a chronic lung disease which causes permanent scarring of lung tissue over time. Current treatment options focus on slowing down the scarring process and increasing quality of life for the patient. However, due to the variable rate of progression between patients, and the potential lack of visual signs, there is still a need to develop new methods of assessing CT scans in order to obtain more accurate prediction results. This paper evaluates a convolutional neural network (CNN)-based method to predict the disease progression using the path of Forced Vital Capacity (FVC) decline, with the hopes that it could better aid doctors in managing the care of patients suffering from PF. The proposed model uses a ResNet50 backbone to predict a linear rate of FVC decline from inputs consisting of a baseline CT scan, and tabular patient data. The predicted gradient will then be used to calculate the predicted FVC value on a weekly basis. The model is trained and evaluated on a dataset obtained from the OSIC Pulmonary Fibrosis Progression challenge, which also served as the benchmark for the evaluation of the proposed model. The proposed model was not able to obtain any improvement over the state-of-the-art solutions, and changes to the current models are suggested for future improvement. Bachelor of Engineering (Computer Science) 2023-05-08T06:40:42Z 2023-05-08T06:40:42Z 2023 Final Year Project (FYP) Wu, J. (2023). Pulmonary fibrosis progression prediction based on ResNet50 architecture. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166528 https://hdl.handle.net/10356/166528 en SCSE22-0429 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computer applications::Life and medical sciences Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Wu, Jingyuan Pulmonary fibrosis progression prediction based on ResNet50 architecture |
| title | Pulmonary fibrosis progression prediction based on ResNet50 architecture |
| title_full | Pulmonary fibrosis progression prediction based on ResNet50 architecture |
| title_fullStr | Pulmonary fibrosis progression prediction based on ResNet50 architecture |
| title_full_unstemmed | Pulmonary fibrosis progression prediction based on ResNet50 architecture |
| title_short | Pulmonary fibrosis progression prediction based on ResNet50 architecture |
| title_sort | pulmonary fibrosis progression prediction based on resnet50 architecture |
| topic | Engineering::Computer science and engineering::Computer applications::Life and medical sciences Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/166528 |
| work_keys_str_mv | AT wujingyuan pulmonaryfibrosisprogressionpredictionbasedonresnet50architecture |