Convolutional neural network-based analysis of pediatric chest X-ray images for pneumonia detection
Detection of pneumonia is generally done using chest X-ray images, traditionally assessed manually by radiologists. However, this method has limitations, including the potential for human error, variability in interpretation, and a shortage of skilled professionals, particularly in resource-limited...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
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Journal of Emerging Investigators, Inc
2024
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| _version_ | 1817931325971103744 |
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| author | Ma, Y Gonzales, RA |
| author_facet | Ma, Y Gonzales, RA |
| author_sort | Ma, Y |
| collection | OXFORD |
| description | Detection of pneumonia is generally done using chest X-ray images, traditionally assessed manually by radiologists. However, this method has limitations, including the potential for human error, variability in interpretation, and a shortage of skilled professionals, particularly in resource-limited settings. Prompted by these challenges and the increasing potential of machine learning in medical diagnostics, we investigated the efficacy of advanced computational models in distinguishing between normal and pneumonia-affected lung images. We hypothesized that an adapted version of the VGG16 model, a convolutional neural network (CNN), would outperform the standard VGG16 and simpler Multilayer Perceptrons (MLPs) in terms of accuracy and reliability. Utilizing a dataset from the Guangzhou Women and Children’s Medical Center, we evaluated the performance of these three models on pediatric chest X-ray images. The MLP showed moderate effectiveness with 78.4% accuracy but struggled with complex image data. The standard VGG16 achieved better results with 90.9% accuracy but displayed overfitting tendencies. The adapted VGG16 model, with reduced filter sizes and dropout layers, demonstrated the highest accuracy at 95.6%, indicating superior performance and stability. These findings suggest that tailored deep learning models like the adapted VGG16 can significantly enhance pneumonia diagnosis from chest X-ray images, offering a balance of accuracy, efficiency, and generalizability. This advancement holds substantial implications for improving diagnostic processes in pediatric healthcare, particularly in settings with limited resources. |
| first_indexed | 2024-12-09T03:20:14Z |
| format | Journal article |
| id | oxford-uuid:5f9f1366-8ace-4fa9-b2f8-26d7788af40f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:20:14Z |
| publishDate | 2024 |
| publisher | Journal of Emerging Investigators, Inc |
| record_format | dspace |
| spelling | oxford-uuid:5f9f1366-8ace-4fa9-b2f8-26d7788af40f2024-11-06T18:05:08ZConvolutional neural network-based analysis of pediatric chest X-ray images for pneumonia detectionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:5f9f1366-8ace-4fa9-b2f8-26d7788af40fEnglishSymplectic ElementsJournal of Emerging Investigators, Inc2024Ma, YGonzales, RADetection of pneumonia is generally done using chest X-ray images, traditionally assessed manually by radiologists. However, this method has limitations, including the potential for human error, variability in interpretation, and a shortage of skilled professionals, particularly in resource-limited settings. Prompted by these challenges and the increasing potential of machine learning in medical diagnostics, we investigated the efficacy of advanced computational models in distinguishing between normal and pneumonia-affected lung images. We hypothesized that an adapted version of the VGG16 model, a convolutional neural network (CNN), would outperform the standard VGG16 and simpler Multilayer Perceptrons (MLPs) in terms of accuracy and reliability. Utilizing a dataset from the Guangzhou Women and Children’s Medical Center, we evaluated the performance of these three models on pediatric chest X-ray images. The MLP showed moderate effectiveness with 78.4% accuracy but struggled with complex image data. The standard VGG16 achieved better results with 90.9% accuracy but displayed overfitting tendencies. The adapted VGG16 model, with reduced filter sizes and dropout layers, demonstrated the highest accuracy at 95.6%, indicating superior performance and stability. These findings suggest that tailored deep learning models like the adapted VGG16 can significantly enhance pneumonia diagnosis from chest X-ray images, offering a balance of accuracy, efficiency, and generalizability. This advancement holds substantial implications for improving diagnostic processes in pediatric healthcare, particularly in settings with limited resources. |
| spellingShingle | Ma, Y Gonzales, RA Convolutional neural network-based analysis of pediatric chest X-ray images for pneumonia detection |
| title | Convolutional neural network-based analysis of pediatric chest X-ray images for pneumonia detection |
| title_full | Convolutional neural network-based analysis of pediatric chest X-ray images for pneumonia detection |
| title_fullStr | Convolutional neural network-based analysis of pediatric chest X-ray images for pneumonia detection |
| title_full_unstemmed | Convolutional neural network-based analysis of pediatric chest X-ray images for pneumonia detection |
| title_short | Convolutional neural network-based analysis of pediatric chest X-ray images for pneumonia detection |
| title_sort | convolutional neural network based analysis of pediatric chest x ray images for pneumonia detection |
| work_keys_str_mv | AT may convolutionalneuralnetworkbasedanalysisofpediatricchestxrayimagesforpneumoniadetection AT gonzalesra convolutionalneuralnetworkbasedanalysisofpediatricchestxrayimagesforpneumoniadetection |