Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification
<b>Background:</b> This study aimed to evaluate the effect of sample size on the development of a three-dimensional convolutional neural network (3DCNN) model for predicting the binary classification of three types of intracranial hemorrhage (ICH): intraparenchymal, subarachnoid, and sub...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/15/2/216 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588715491328000 |
|---|---|
| author | Chun-Chao Huang Hsin-Fan Chiang Cheng-Chih Hsieh Bo-Rui Zhu Wen-Jie Wu Jin-Siang Shaw |
| author_facet | Chun-Chao Huang Hsin-Fan Chiang Cheng-Chih Hsieh Bo-Rui Zhu Wen-Jie Wu Jin-Siang Shaw |
| author_sort | Chun-Chao Huang |
| collection | DOAJ |
| description | <b>Background:</b> This study aimed to evaluate the effect of sample size on the development of a three-dimensional convolutional neural network (3DCNN) model for predicting the binary classification of three types of intracranial hemorrhage (ICH): intraparenchymal, subarachnoid, and subdural (IPH, SAH, SDH, respectively). <b>Methods:</b> During the training, we compiled all images of each brain computed tomography scan into a single 3D image, which was then fed into the model to classify the presence of ICH. We divided the non-hemorrhage quantities into 20, 30, 40, 50, 100, and 150 and the ICH quantities into 20, 30, 40, and 50. Cross-validation was performed to compute the average area under the curve (AUC) over the last five iterations. The AUC and accuracy were used to evaluate the performance of the models. <b>Results:</b> Fifty patients, each with the three ICH types, and 150 non-hemorrhage cases were enrolled. Larger sample sizes achieved stable and acceptable performance in the artificial intelligence (AI) models, whereas training with a limited number of cases posed the risk of falsely high AUC values or accuracy. The overall trends and fluctuations in AUC values were similar between IPH and SDH but different for SAH. The accuracy of the results was relatively consistent among the three ICH types. <b>Conclusions:</b> The 3DCNN technique can be used to develop AI models capable of detecting ICH from limited case numbers. However, a minimal case number must be provided. The performance of AI models varies across different ICH types and is more stable with larger sample sizes. |
| format | Article |
| id | doaj-art-9f7ab19a429e4ef0bbb66e46ac5d1fbb |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-9f7ab19a429e4ef0bbb66e46ac5d1fbb2025-01-24T13:29:08ZengMDPI AGDiagnostics2075-44182025-01-0115221610.3390/diagnostics15020216Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage ClassificationChun-Chao Huang0Hsin-Fan Chiang1Cheng-Chih Hsieh2Bo-Rui Zhu3Wen-Jie Wu4Jin-Siang Shaw5Department of Radiology, MacKay Memorial Hospital, Taipei 104, TaiwanDepartment of Radiology, MacKay Memorial Hospital, Taipei 104, TaiwanDepartment of Radiology, MacKay Memorial Hospital, Taipei 104, TaiwanInstitute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106, TaiwanInstitute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106, TaiwanInstitute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106, Taiwan<b>Background:</b> This study aimed to evaluate the effect of sample size on the development of a three-dimensional convolutional neural network (3DCNN) model for predicting the binary classification of three types of intracranial hemorrhage (ICH): intraparenchymal, subarachnoid, and subdural (IPH, SAH, SDH, respectively). <b>Methods:</b> During the training, we compiled all images of each brain computed tomography scan into a single 3D image, which was then fed into the model to classify the presence of ICH. We divided the non-hemorrhage quantities into 20, 30, 40, 50, 100, and 150 and the ICH quantities into 20, 30, 40, and 50. Cross-validation was performed to compute the average area under the curve (AUC) over the last five iterations. The AUC and accuracy were used to evaluate the performance of the models. <b>Results:</b> Fifty patients, each with the three ICH types, and 150 non-hemorrhage cases were enrolled. Larger sample sizes achieved stable and acceptable performance in the artificial intelligence (AI) models, whereas training with a limited number of cases posed the risk of falsely high AUC values or accuracy. The overall trends and fluctuations in AUC values were similar between IPH and SDH but different for SAH. The accuracy of the results was relatively consistent among the three ICH types. <b>Conclusions:</b> The 3DCNN technique can be used to develop AI models capable of detecting ICH from limited case numbers. However, a minimal case number must be provided. The performance of AI models varies across different ICH types and is more stable with larger sample sizes.https://www.mdpi.com/2075-4418/15/2/216intracranial hemorrhagedeep learning3D convolutional neural networksartificial intelligenceCT |
| spellingShingle | Chun-Chao Huang Hsin-Fan Chiang Cheng-Chih Hsieh Bo-Rui Zhu Wen-Jie Wu Jin-Siang Shaw Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification Diagnostics intracranial hemorrhage deep learning 3D convolutional neural networks artificial intelligence CT |
| title | Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification |
| title_full | Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification |
| title_fullStr | Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification |
| title_full_unstemmed | Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification |
| title_short | Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification |
| title_sort | impact of dataset size on 3d cnn performance in intracranial hemorrhage classification |
| topic | intracranial hemorrhage deep learning 3D convolutional neural networks artificial intelligence CT |
| url | https://www.mdpi.com/2075-4418/15/2/216 |
| work_keys_str_mv | AT chunchaohuang impactofdatasetsizeon3dcnnperformanceinintracranialhemorrhageclassification AT hsinfanchiang impactofdatasetsizeon3dcnnperformanceinintracranialhemorrhageclassification AT chengchihhsieh impactofdatasetsizeon3dcnnperformanceinintracranialhemorrhageclassification AT boruizhu impactofdatasetsizeon3dcnnperformanceinintracranialhemorrhageclassification AT wenjiewu impactofdatasetsizeon3dcnnperformanceinintracranialhemorrhageclassification AT jinsiangshaw impactofdatasetsizeon3dcnnperformanceinintracranialhemorrhageclassification |