On the detection of pneumonia using deep learning and explainability
| dc.contributor.author | Chaa, Amina | |
| dc.contributor.author | Dilmi, Noussaiba | |
| dc.contributor.author | Debbi, Hichem: Supervisor | |
| dc.date.accessioned | 2024-07-15T11:00:12Z | |
| dc.date.available | 2024-07-15T11:00:12Z | |
| dc.date.issued | 2024-06 | |
| dc.description.abstract | Pneumonia detection from chest X-ray images is achieved using the pre-trained VGG16 deep learning model, known for its ability to extract relevant features from images. The process involves feeding the X-ray images into the VGG16 model, which then analyzes and classifies them to detect signs of pneumonia. To build trust in the model’s decisions, explainability techniques such as Causal Explanation CNN (CexCNN) and CAM are employed. These techniques provide visual explanations by highlighting the regions of the X-ray images that the model focuses on when making its predictions. CexCNN offers casual robust explanations, indicating which features are most influential in the decision-making process, thus leading to gain confidence in the CNN model. Which is very crucial especially in the medical field. | |
| dc.identifier.uri | https://dspace.univ-msila.dz/handle/123456789/43793 | |
| dc.language.iso | en | |
| dc.publisher | University Mohamed Boudiaf of M’sila | |
| dc.subject | Deep learning | |
| dc.subject | CNN | |
| dc.subject | CexCNN | |
| dc.subject | VGG16 | |
| dc.subject | ImageNet | |
| dc.subject | Computer vision | |
| dc.subject | heatmaps | |
| dc.subject | Boundingbox | |
| dc.subject | X-Ray | |
| dc.title | On the detection of pneumonia using deep learning and explainability | |
| dc.type | Thesis |