Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910776913035264 |
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| author | Togni, Jimi Attux, Romis |
| author_facet | Togni, Jimi Attux, Romis |
| contents | This study presents a comparative analysis of methods for detecting COVID-19 infection in radiographic images. The images, sourced from publicly available datasets, were categorized into three classes: 'normal,' 'pneumonia,' and 'COVID.' For the experiments, transfer learning was employed using eight pre-trained networks: SqueezeNet, DenseNet, ResNet, AlexNet, VGG, GoogleNet, ShuffleNet, and MobileNet. DenseNet achieved the highest accuracy of 97.64% using the ADAM optimization function in the multiclass approach. In the binary classification approach, the highest precision was 99.98%, obtained by the VGG, ResNet, and MobileNet networks. A comparative evaluation was also conducted using heat maps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04196 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection Togni, Jimi Attux, Romis Image and Video Processing Machine Learning 68T07 I.2.10; I.4.8 This study presents a comparative analysis of methods for detecting COVID-19 infection in radiographic images. The images, sourced from publicly available datasets, were categorized into three classes: 'normal,' 'pneumonia,' and 'COVID.' For the experiments, transfer learning was employed using eight pre-trained networks: SqueezeNet, DenseNet, ResNet, AlexNet, VGG, GoogleNet, ShuffleNet, and MobileNet. DenseNet achieved the highest accuracy of 97.64% using the ADAM optimization function in the multiclass approach. In the binary classification approach, the highest precision was 99.98%, obtained by the VGG, ResNet, and MobileNet networks. A comparative evaluation was also conducted using heat maps. |
| title | Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection |
| topic | Image and Video Processing Machine Learning 68T07 I.2.10; I.4.8 |
| url | https://arxiv.org/abs/2501.04196 |