深度残差CNN用于多类别胸部感染诊断
Deep Residual CNN for Multi-Class Chest Infection Diagnosis
- Hana Academy Seoul（韩娜首尔学院）
- Korea Science Academy of KAIST（韩国科学技术院附属韩国科学学院）
- Sungkyunkwan University（成均馆大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究开发并评估了一种深度残差CNN，用于基于胸部X射线图像的多类别胸部感染诊断，在合并数据集上达到93%的总体准确率，并指出纤维化类别性能差异及未来优化方向。
AI中文摘要：
深度学习的出现显著推动了自动化医学图像诊断的能力，为医疗保健和医学诊断领域提供了宝贵的工具和资源。本研究深入探讨了利用胸部X射线图像进行多类别胸部感染诊断的深度残差卷积神经网络（CNN）的开发与评估。所实现的模型在来自不同来源合并的数据集上进行训练和验证，展示了93%的稳健总体准确率。然而，不同类别（尤其是纤维化）之间的性能差异凸显了自动化医学图像诊断所固有的复杂性和挑战。所获得的见解为未来研究铺平了道路，重点在于增强模型对图像中呈现更细微和微妙视觉特征的病症进行分类的能力，以及优化和改进模型架构和训练过程。本文对模型的开发、实施和评估进行了全面探索，为该领域的未来研究和开发提供了见解和方向。
英文摘要：
The advent of deep learning has significantly propelled the capabilities of automated medical image diagnosis, providing valuable tools and resources in the realm of healthcare and medical diagnostics. This research delves into the development and evaluation of a Deep Residual Convolutional Neural Network (CNN) for the multi-class diagnosis of chest infections, utilizing chest X-ray images. The implemented model, trained and validated on a dataset amalgamated from diverse sources, demonstrated a robust overall accuracy of 93%. However, nuanced disparities in performance across different classes, particularly Fibrosis, underscored the complexity and challenges inherent in automated medical image diagnosis. The insights derived pave the way for future research, focusing on enhancing the model's proficiency in classifying conditions that present more subtle and nuanced visual features in the images, as well as optimizing and refining the model architecture and training process. This paper provides a comprehensive exploration into the development, implementation, and evaluation of the model, offering insights and directions for future research and development in the field.
