通过集成HOG和深度特征提升组织病理图像分类性能
Enhancing Histopathological Image Classification via Integrated HOG and Deep Features with Robust Noise Performance
AI总结：
本文研究了在LC25000数据集上使用HOG和深度特征提升图像分类性能，发现深度特征提取模型在噪声环境下具有更强鲁棒性。
摘要：
数字病理学时代推动了组织病理学检查的发展，使自动化图像分析在临床实践中变得至关重要。本研究评估了机器学习和深度学习模型在LC25000数据集上的分类性能，该数据集包含五类组织病理图像。我们使用微调的InceptionResNet-v2网络作为分类器和特征提取器。我们的结果表明，微调的InceptionResNet-v2实现了96.01%的分类准确率和96.8%的平均AUC。训练于InceptionResNet-v2深度特征的模型优于仅使用预训练网络的模型，其中神经网络模型实现了99.99%的AUC和99.84%的准确率。在不同SNR条件下评估模型鲁棒性时发现，使用深度特征的模型表现出更强的鲁棒性，特别是GBM和KNN。HOG和深度特征的结合显示了增强的性能，但在噪声环境中效果较弱。
英文摘要：
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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