融合纹理与深度特征提升病理图像分类准确率
Enhancing Histopathological Image Classification via Integrated HOG and Deep Features with Robust Noise Performance
- 结合HOG纹理特征与InceptionResNet-v2深度特征进行分类
- 深度特征训练模型达99.84%准确率，AUC达99.99%
- 在低信噪比下仍保持稳定，适合临床噪声环境
数字病理时代推动了自动图像分析在临床中的应用。本研究在包含五类组织的LC25000数据集上评估机器学习与深度学习模型的分类性能。采用微调后的InceptionResNet-v2作为分类器和特征提取器，其分类准确率达96.01%，平均AUC为96.8%。基于InceptionResNet-v2提取的深度特征训练的模型优于仅使用预训练网络的模型，其中神经网络模型达到99.84%准确率与99.99% AUC。在不同信噪比（SNR）条件下评估模型鲁棒性，发现深度特征模型更具抗噪能力，尤其在GBM与KNN模型中表现突出。虽HOG与深度特征融合可提升性能，但在高噪声环境下优势减弱。
原文摘要 · Abstract (English)
The era of digital pathology has advanced histopathological examinations, making automated image analysis essential in clinical practice. This study evaluates the classification performance of machine learning and deep learning models on the LC25000 dataset, which includes five classes of histopathological images. We used the fine-tuned InceptionResNet-v2 network both as a classifier and for feature extraction. Our results show that the fine-tuned InceptionResNet-v2 achieved a classification accuracy of 96.01\% and an average AUC of 96.8\%. Models trained on deep features from InceptionResNet-v2 outperformed those using only the pre-trained network, with the Neural Network model achieving an AUC of 99.99\% and accuracy of 99.84\%. Evaluating model robustness under varying SNR conditions revealed that models using deep features exhibited greater resilience, particularly GBM and KNN. The combination of HOG and deep features showed enhanced performance, however, less so in noisy environments.
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