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Pneumonia detection by deep learning models based on image processing method: A novel approach |
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| รหัสดีโอไอ | |
| Creator | Ahmet Celik |
| Title | Pneumonia detection by deep learning models based on image processing method: A novel approach |
| Contributor | Semih Demirel |
| Publisher | Maejo University |
| Publication Year | 2567 |
| Journal Title | Maejo International Journal of Science and Technology |
| Journal Vol. | 18 |
| Journal No. | 1 |
| Page no. | 75 |
| Keyword | pneumonia detection, chest X-Ray image, histogram equalisation, mask R-CNN, image segmentation, deep learning |
| Website title | Maejo International Journal of Science and Technology |
| ISSN | 1905-7873 |
| Abstract | Pneumonia is a common and challenging disease to treat. Diagnosis of pneumonia is performed by analysing chest X-ray images with a specialist doctor today. This situation can create an excessive workload for doctors and prolong the diagnosis time. Performing early and accurate diagnosis of pneumonia using pre-trained deep learning models, which are a subcategory of the deep learning method, can be extremely beneficial. Using computer-aided diagnosis systems increases the accuracy of pneumonia diagnosis and thanks to these systems, doctors have an idea about the disease before diagnosis. In this study chest X-Ray images were classified as healthy or pneumonia using pre-trained deep learning methods. The histogram equalisation image processing method was used to improve image quality and the mask region-based convolutional neural network pre-trained method was used to segment the chest region. Alexnet, ResNet18 and VGG16 pre-trained models were used for image classification as healthy and pneumonia. ResNet18 showed outstanding performance in this study. According to the performance metrics of accuracy (0.983), recall (0.994) and F1-score (0.987), success rates were achieved by using the ResNet18 model. This study has shown that deep learning models can achieve high success rates in pneumonia diagnosis. |
