Colorectal cancer (CRC) is the second most diagnosed cancer in the United States. It is identified by histopathological evaluations of microscopic images of the cancerous region, relying on a subjective interpretation. The Colorectal Histology dataset used in this study contains 5000 images, made available by the University Medical Center Mannheim. This approach proposes the automatic identification of eight types of tissues found in CRC histopathological evaluation. We apply Transfer Learning from architectures of Convolutional Neural Networks (CNNs). We modify the structures of CNNs to extract features from the images and input them to well-known machine learning methods: Naive Bayes, Multilayer Perceptron, k-Nearest Neighbors, Random Forest, and Support Vector Machine (SVM). We evaluated 108 extractor–classifier combinations. The one that achieved the best results is DenseNet169 with SVM (RBF), reaching an Accuracy of 92.083% and F1-Score of 92.117%. Therefore, our approach is capable of distinguishing tissues found in CRC histopathological evaluation.

结直肠癌（CRC）是美国第二大被诊断为癌症的癌症。通过对癌变区域的显微图像的组织病理学评估，可以依靠主观解释来识别。这项研究中使用的结直肠组织学数据集包含5000张图像，由曼海姆大学医学中心提供。该方法提出了在CRC组织病理学评估中发现的八种类型组织的自动识别。我们从卷积神经网络（CNN）的架构中应用转移学习。我们修改CNN的结构以从图像中提取特征，并将其输入到著名的机器学习方法中：朴素贝叶斯，多层感知器，k最近邻居，随机森林和支持向量机（SVM）。我们评估了108个提取器-分类器组合。取得最佳结果的是带有SVM（RBF）的DenseNet169，其准确度达到92.083％，F1得分达到92.117％。因此，我们的方法能够区分在CRC组织病理学评估中发现的组织。