## 1 Introduction
Other investigations approached the recognition of microstructures from the point of view of the inference of material properties, as in the case of [PERSON] et al. [15; 16], where steel properties, such as the stress-strain behavior, and particularly, the UTS and the total elongation, were obtained from micrographs of different steels using deep-learning techniques. Many papers have tackled classification problems using deep-learning and traditional machine-learning approaches, in different contexts for performance comparison. [PERSON] et al. [17] used deep learning for sentiment analysis and showed better accuracy compared with traditional machine-learning models. Other studies, such as [PERSON] et al. [18], evaluated SVM and a narrow CNN (two dense layers) for two different datasets, and they concluded that, for a large dataset (MNIST) the deep-learning approach performed better than traditional ML; however, when using a small sample dataset (COREL1000), the accuracy of SVM was slightly better than the CNN. [PERSON] et al. [19] reviewed the effects of imbalanced data disparity with a MINST handwritten dataset. In the experiments, a deep belief network (DBN) is used, and the results were compared with conventional ML algorithms, such as backpropagation neural network, SVM, decision trees and naive Bayes. This research concluded that DBN achieved a high accuracy rate and low error according to the performance metrics as compared to the other ML algorithms, which are more affected by data imbalance. As seen so far, there are several ways to approach the classification of steel microstructures using machine-learning models. Each research work proposes one or several models with different levels of success. However, it is difficult to determine the best algorithm to be used with steel microstructures because the data are obtained with different technologies (optical or electronic) and are not sufficiently extensive and homogeneous to create models that manage to generalize the predictions and avoid overfitting. In this work, we take into consideration optical images and compare classical and deep-learning models to obtain the best strategy to be established as a starting point for future research. Thus, the present work aims to perform experiments for creating different models generated by supervised machine-learning techniques to classify the microstructures of steels that have been subjected to several thermal treatments. We create our dataset based on previously labeled steel microstructures, and all the images are taken using optical microscopy. The experiments are on the one hand, based on six classic machine-learning (ML) algorithms and, on the other hand, by working with a transfer learning scheme based on the deep-learning networks \"GoogLeNet\" and \"ResNet50\". The images for creating the machine-learning models were obtained from three sets of hypoautectoid carbon steel specimens subjected to annealing, quenching and quenching plus tempering heat treatments. Image classification was performed for the three categories, one for each heat treatment, based on the hypothesis that each treatment has a characteristic microstructure.