Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable morphological estimate from galaxy images. Here, we investigate how to substantially improve the galaxy classification within large datasets by mimicking human classification. We combine accurate visual classifications from the Galaxy Zoo project with machine and deep learning methodologies. We propose two distinct approaches for galaxy morphology: one based on non-parametric morphology and traditional machine learning algorithms; and another based on Deep Learning. To measure the input features for the traditional machine learning methodology, we have developed a system called CyMorph, with a novel non-parametric approach to study galaxy morphology. The main datasets employed comes from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). We also discuss the class imbalance problem considering three classes. Performance of each model is mainly measured by Overall Accuracy (OA). A spectroscopic validation with astrophysical parameters is also provided for Decision Tree models to assess the quality of our morphological classification. In all of our samples, both Deep and Traditional Machine Learning approaches have over 94.5% OA to classify galaxies in two classes (elliptical and spiral). We compare our classification with state-of-the-art morphological classification from literature. Considering only two classes separation, we achieve 99% of overall accuracy in average when using our deep learning models, and 82% when using three classes. We provide a catalog with 670,560 galaxies containing our best results, including morphological metrics and classification.

形态分类是定义星系样本的关键信息，旨在研究宇宙的大规模结构。本质上，挑战在于建立可靠的方法，以根据星系图像执行可靠的形态估计。在这里，我们研究如何通过模仿人类分类来大幅改善大型数据集中的星系分类。我们将来自Galaxy Zoo项目的准确视觉分类与机器和深度学习方法相结合。我们提出了两种不同的星系形态方法：一种基于非参数形态学和传统机器学习算法；另一个基于深度学习。为了衡量传统机器学习方法的输入特征，我们开发了一个名为CyMorph的系统，用一种新颖的非参数方法来研究星系形态。所采用的主要数据集来自Sloan Digital Sky Survey Data Release 7（SDSS-DR7）。我们还讨论了考虑三个班级的班级失衡问题。每个模型的性能主要由总体精度（OA）衡量。还为决策树模型提供了具有天体参数的光谱验证，以评估我们的形态学分类的质量。在我们所有的样本中，深度和传统机器学习方法都具有超过94.5％的OA，可以将星系分为两类（椭圆形和螺旋形）。我们将分类与文献中最新的形态学分类进行比较。仅考虑两个类别的分离，使用深度学习模型时，我们平均可以达到整体准确性的99％，而使用三个班级则占82％。我们提供包含670,560个星系的目录，其中包含我们的最佳结果，包括形态指标和分类。