用DenseNet121等方法提升胸部X光片多疾病分类准确率
Classification of Chest XRay Diseases through image processing and analysis techniques
- 采用DenseNet121等模型结合图像处理技术进行多类疾病识别
- 在ChestX-ray8数据集上达到92.3%的平均分类准确率
- 开源代码与网页应用，适合医疗AI初学者参考
胸部X光片是诊断胸腔疾病最常用的影像检查方式之一。本研究系统评估了多种图像分析方法在多类别胸部X光疾病分类中的表现，重点采用DenseNet121模型。实验在ChestX-ray8公开数据集上进行，结果显示所提方法平均分类准确率达92.3%。研究还开发了一款开源的基于Web的应用程序，用于可视化模型推理过程。我们深入分析了各方法的局限性，并提出未来改进方向。完整代码已公开于GitHub。
原文摘要 · Abstract (English)
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
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