This paper presents a method for pixel-wise classification applied for the first time on hippocampus histological images. The goal is achieved by representing pixels in a 14-D vector, composed of grey-level information and moment invariants. Then, several popular machine learning models are used to categorize them, and multiple metrics are computed to evaluate the performance of the different models. The multilayer perceptron, random forest, support vector machine, and radial basis function networks were compared, achieving the multilayer perceptron model the highest result on accuracy metric, AUC, and score with highly satisfactory results for substituting a manual classification task, due to an expert opinion in the hippocampus histological images.

中文翻译：

---

海马组织学图像中的像素分类

本文提出了一种首次应用于海马组织学图像的逐像素分类方法。该目标是通过在 14 维向量中表示像素来实现的，该向量由灰度信息和矩不变量组成。然后，使用几种流行的机器学习模型对它们进行分类，并计算多个指标来评估不同模型的性能。对多层感知器、随机森林、支持向量机和径向基函数网络进行了比较，实现了多层感知器模型在准确度度量、AUC 和分数上的最高结果，由于专家的帮助，替代人工分类任务的结果非常令人满意海马组织学图像中的意见。