Studies recently accomplished on the Enteric Nervous System have shown that chronic degenerative diseases affect the Enteric Glial Cells (EGC) and, thus, the development of recognition methods able to identify whether or not the EGC are affected by these type of diseases may be helpful in its diagnoses. In this work, we propose the use of pattern recognition and machine learning techniques to evaluate if a given animal EGC image was obtained from a healthy individual or one affect by a chronic degenerative disease. In the proposed approach, we have performed the classification task with handcrafted features and deep learning-based techniques, also known as non-handcrafted features. The handcrafted features were obtained from the textural content of the ECG images using texture descriptors, such as the Local Binary Pattern (LBP). Moreover, the representation learning techniques employed in the approach are based on different Convolutional Neural Network (CNN) architectures, such as AlexNet and VGG16, with and without transfer learning. The complementarity between the handcrafted and non-handcrafted features was also evaluated with late fusion techniques. The datasets of EGC images used in the experiments, which are also contributions of this paper, are composed of three different chronic degenerative diseases: Cancer, Diabetes Mellitus, and Rheumatoid Arthritis. The experimental results, supported by statistical analysis, show that the proposed approach can distinguish healthy cells from the sick ones with a recognition rate of 89.30% (Rheumatoid Arthritis), 98.45% (Cancer), and 95.13% (Diabetes Mellitus), being achieved by combining classifiers obtained on both feature scenarios.

最近对肠神经系统进行的研究表明，慢性退行性疾病会影响肠神经胶质细胞 (EGC)，因此，开发能够识别 EGC 是否受这些疾病影响的识别方法可能有助于它的诊断。在这项工作中，我们建议使用模式识别和机器学习技术来评估给定的动物 EGC 图像是否来自健康个体或受慢性退行性疾病的影响。在所提出的方法中，我们使用手工特征和基于深度学习的技术（也称为非手工特征）执行了分类任务。手工制作的特征是使用纹理描述符（例如局部二进制模式（LBP））从 ECG 图像的纹理内容中获得的。而且，该方法中采用的表示学习技术基于不同的卷积神经网络 (CNN) 架构，例如 AlexNet 和 VGG16，有和没有迁移学习。手工和非手工特征之间的互补性也通过后期融合技术进行了评估。实验中使用的 EGC 图像数据集也是本文的贡献，由三种不同的慢性退行性疾病组成：癌症、糖尿病和类风湿性关节炎。统计分析支持的实验结果表明，所提出的方法可以区分健康细胞和患病细胞，识别率分别为 89.30%（类风湿性关节炎）、98.45%（癌症）和 95.13%（糖尿病），正在实现通过组合在两个特征场景上获得的分类器。