Optimized CNN-based Diagnosis System to Detect the Pneumonia from Chest Radiographs
| Autor: | Shelby Joji, Mohammed Aledhari, Mohamed Hefeida, Fahad Saeed |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
medicine.diagnostic_test business.industry Radiography Deep learning Early detection 010501 environmental sciences Overfitting medicine.disease Machine learning computer.software_genre 01 natural sciences Convolutional neural network 03 medical and health sciences Pneumonia Lung segmentation medicine Artificial intelligence Chest radiograph business computer 030304 developmental biology 0105 earth and related environmental sciences |
| Zdroj: | BIBM |
| DOI: | 10.1109/bibm47256.2019.8983114 |
| Popis: | Pneumonia is a high mortality disease that kills 50, 000 people in the United States each year. Children under the age of 5 and older population over the age of 65 are susceptible to serious cases of pneumonia. The United States spend billions of dollars fighting pneumonia-related infections every year. Early detection and intervention are crucial in treating pneumonia related infections. Since chest x-ray is one of the simplest and cheapest methods to diagnose pneumonia, we propose a deep learning algorithm based on convolutional neural networks to identify and classify pneumonia cases from these images. For all three models implemented, we obtained varying classification results and accuracy. Based on the results, we obtained better prediction with average accuracy of (68%) and average specificity of (69%) in contrast to the current state-of-the-art accuracy that is (51%) using the Visual Geometry Group (VGG16 also called OxfordNet), which is a convolutional neural network architecture developed by the Visual Geometry Group of Oxford. By implementing more novel lung segmentation techniques, reducing over fitting, and adding more learning layers, the proposed model has the potential to predict at higher accuracy than human specialists and will help subsidies and reduce the cost of diagnosis across the globe. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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