Classification of Chest XRay Diseases through image processing and analysis techniques
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909992043413504 |
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| author | Novoa, Santiago Martínez Ibáñez, María Catalina Mesa, Lina Gómez Kramer, Jeremias |
| author_facet | Novoa, Santiago Martínez Ibáñez, María Catalina Mesa, Lina Gómez Kramer, Jeremias |
| contents | Multi-Classification Chest X-Ray Images are one of the most prevalent forms of radiological examination used for diagnosing thoracic diseases. In this study, we offer a concise overview of several methods employed for tackling this task, including DenseNet121. In addition, we deploy an open-source web-based application. In our study, we conduct tests to compare different methods and see how well they work. We also look closely at the weaknesses of the methods we propose and suggest ideas for making them better in the future. Our code is available at: https://github.com/AML4206-MINE20242/Proyecto_AML |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_10913 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Classification of Chest XRay Diseases through image processing and analysis techniques Novoa, Santiago Martínez Ibáñez, María Catalina Mesa, Lina Gómez Kramer, Jeremias Computer Vision and Pattern Recognition Multi-Classification Chest X-Ray Images are one of the most prevalent forms of radiological examination used for diagnosing thoracic diseases. In this study, we offer a concise overview of several methods employed for tackling this task, including DenseNet121. In addition, we deploy an open-source web-based application. In our study, we conduct tests to compare different methods and see how well they work. We also look closely at the weaknesses of the methods we propose and suggest ideas for making them better in the future. Our code is available at: https://github.com/AML4206-MINE20242/Proyecto_AML |
| title | Classification of Chest XRay Diseases through image processing and analysis techniques |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.10913 |