Recently, research has been conducted to assist in the processing and analysis of histopathological images using machine learning algorithms. In this study, we established machine learning-based algorithms to detect photothrombotic lesions in histological images of photothrombosis-induced rabbit brains. Six machine learning-based algorithms for binary classification were applied, and the accuracies were compared to classify normal tissues and photothrombotic lesions. The lesion classification model consisting of a 3-layered neural network with a rectified linear unit (ReLU) activation function, Xavier initialization, and Adam optimization using datasets with a unit size of 128×128 pixels yielded the highest accuracy (0.975). In the validation using the tested histological images, it was confirmed that the model could identify regions where brain damage occurred due to photochemical ischemic stroke. Through the development of machine learning-based photothrombotic lesion classification models and performance comparisons, we confirmed that machine learning algorithms have the potential to be utilized in histopathology and various medical diagnostic techniques.

中文翻译：

---

基于机器学习的分类在组织学兔脑图像中发现光血栓病变的比较研究

最近，已经进行了研究，以帮助使用机器学习算法处理和分析组织病理学图像。在这项研究中，我们建立了基于机器学习的算法来检测光血栓诱导兔脑组织学图像中的光血栓病变。应用了六种基于机器学习的二元分类算法，并比较了分类正常组织和光血栓病变的准确性。病变分类模型由具有校正线性单元 (ReLU) 激活函数的 3 层神经网络、Xavier 初始化和使用单位大小为的数据集的 Adam 优化组成128×128像素产生最高精度（0.975）。在使用测试的组织学图像进行验证时，证实该模型可以识别由于光化学缺血性中风而发生脑损伤的区域。通过开发基于机器学习的光血栓病变分类模型和性能比较，我们证实机器学习算法有潜力用于组织病理学和各种医学诊断技术。