融合ResNet101与自定义CNN，精准识别肾结石、囊肿和肿瘤
Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors
- 用预训练ResNet101结合自定义CNN进行特征融合
- 测试准确率达100%，训练准确率99.73%
- 适合临床部署的高效自动化肾病诊断
医学图像分类是利用先进计算技术提升疾病诊断与治疗规划的重要研究领域。深度学习模型，尤其是卷积神经网络（CNN），通过自动精确分析复杂医学图像彻底改变了该领域。本研究提出一种混合深度学习模型，将预训练的ResNet101与自定义CNN结合，用于将肾部CT图像分为正常、结石、囊肿和肿瘤四类。该模型通过特征融合提升分类精度，在包含12,446张CT图像的数据集上，训练准确率达99.73%，测试准确率达100%。相比独立的ResNet101，该混合模型在分类性能上表现更优，具备更高的精确率、召回率和更低的测试时间，为自动化肾病诊断提供了稳健高效的解决方案，适用于临床实际应用。
原文摘要 · Abstract (English)
Medical image classification is a vital research area that utilizes advanced computational techniques to improve disease diagnosis and treatment planning. Deep learning models, especially Convolutional Neural Networks (CNNs), have transformed this field by providing automated and precise analysis of complex medical images. This study introduces a hybrid deep learning model that integrates a pre-trained ResNet101 with a custom CNN to classify kidney CT images into four categories: normal, stone, cyst, and tumor. The proposed model leverages feature fusion to enhance classification accuracy, achieving 99.73% training accuracy and 100% testing accuracy. Using a dataset of 12,446 CT images and advanced feature mapping techniques, the hybrid CNN model outperforms standalone ResNet101. This architecture delivers a robust and efficient solution for automated kidney disease diagnosis, providing improved precision, recall, and reduced testing time, making it highly suitable for clinical applications.
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