There is a strong need for automated systems to improve diagnostic quality
and reduce the analysis time in histopathology image processing. Automated
detection and classification of pathological tissue characteristics with
computer-aided diagnostic systems are a critical step in the early diagnosis
and treatment of diseases. Once a pathology image is scanned by a microscope
and loaded onto a computer, it can be used for automated detection and
classification of diseases. In this study, the DenseNet-161 and ResNet-50
pre-trained CNN models have been used to classify digital histopathology
patches into the corresponding whole slide images via transfer learning
technique. The proposed pre-trained models were tested on grayscale and color
histopathology images. The DenseNet-161 pre-trained model achieved a
classification accuracy of 97.89% using grayscale images and the ResNet-50
model obtained the accuracy of 98.87% for color images. The proposed
pre-trained models outperform state-of-the-art methods in all performance
metrics to classify digital pathology patches into 24 categories.

利用 DenseNet-161 和 ResNet-50 预训练 CNN 模型，通过迁移学习的方法对数字组织病理学补丁进行分类，实验结果表明，这种方法可以自动检测和分类疾病，并在分类效果方面表现出色。