Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913673701752832 |
|---|---|
| author | Nettur, Suresh Babu Karpurapu, Shanthi Nettur, Unnati Gajja, Likhit Sagar Myneni, Sravanthy Dusi, Akhil Posham, Lalithya |
| author_facet | Nettur, Suresh Babu Karpurapu, Shanthi Nettur, Unnati Gajja, Likhit Sagar Myneni, Sravanthy Dusi, Akhil Posham, Lalithya |
| contents | Pneumonia is a leading cause of illness and death in children, underscoring the need for early and accurate detection. In this study, we propose a novel lightweight ensemble model for detecting pneumonia in children using chest X-ray images. This ensemble model integrates two pre-trained convolutional neural networks (CNNs), MobileNetV2 and NASNetMobile, selected for their balance of computational efficiency and accuracy. These models were fine-tuned on a pediatric chest X-ray dataset and combined to enhance classification performance. Our proposed ensemble model achieved a classification accuracy of 98.63%, significantly outperforming individual models such as MobileNetV2 (97.10%) and NASNetMobile(96.25%) in terms of accuracy, precision, recall, and F1 score. Moreover, the ensemble model outperformed state-of-the-art architectures, including ResNet50, InceptionV3, and DenseNet201, while maintaining computational efficiency. The proposed lightweight ensemble model presents a highly effective and resource-efficient solution for pneumonia detection, making it particularly suitable for deployment in resource-constrained settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16249 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images Nettur, Suresh Babu Karpurapu, Shanthi Nettur, Unnati Gajja, Likhit Sagar Myneni, Sravanthy Dusi, Akhil Posham, Lalithya Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Pneumonia is a leading cause of illness and death in children, underscoring the need for early and accurate detection. In this study, we propose a novel lightweight ensemble model for detecting pneumonia in children using chest X-ray images. This ensemble model integrates two pre-trained convolutional neural networks (CNNs), MobileNetV2 and NASNetMobile, selected for their balance of computational efficiency and accuracy. These models were fine-tuned on a pediatric chest X-ray dataset and combined to enhance classification performance. Our proposed ensemble model achieved a classification accuracy of 98.63%, significantly outperforming individual models such as MobileNetV2 (97.10%) and NASNetMobile(96.25%) in terms of accuracy, precision, recall, and F1 score. Moreover, the ensemble model outperformed state-of-the-art architectures, including ResNet50, InceptionV3, and DenseNet201, while maintaining computational efficiency. The proposed lightweight ensemble model presents a highly effective and resource-efficient solution for pneumonia detection, making it particularly suitable for deployment in resource-constrained settings. |
| title | Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.16249 |