Tuberculosis (TB) is is an infectious disease that generally attacks the lungs and causes death for millions of people annually. Chest radiography and deep-learning-based image segmentation techniques can be utilized for TB diagnostics. Convolutional Neural Networks (CNNs) has shown advantages in medical image recognition applications as powerful models to extract informative features from images. Here, we present a novel hybrid method for efficient classification of chest X-ray images. First, the features are extracted from chest X-ray images using MobileNet, a CNN model, which was previously trained on the ImageNet dataset. Then, to determine which of these features are the most relevant, we apply the Artificial Ecosystem-based Optimization (AEO) algorithm as a feature selector. The proposed method is applied to two public benchmark datasets (Shenzhen and Dataset 2) and allows them to achieve high performance and reduced computational time. It selected successfully only the best 25 and 19 (for Shenzhen and Dataset 2, respectively) features out of about 50,000 features extracted with MobileNet, while improving the classification accuracy (90.2% for Shenzen dataset and 94.1% for Dataset 2). The proposed approach outperforms other deep learning methods, while the results are the best compared to other recently published works on both datasets.

中文翻译：

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使用基于人工生态系统的深度神经网络特征优化检测胸片中结核病的新方法

结核病 (TB) 是一种传染病，通常会侵袭肺部，每年导致数百万人死亡。胸片和基于深度学习的图像分割技术可用于结核病诊断。卷积神经网络 (CNN) 作为从图像中提取信息特征的强大模型，已在医学图像识别应用中显示出优势。在这里，我们提出了一种新的混合方法，用于对胸部 X 射线图像进行有效分类。首先，使用之前在 ImageNet 数据集上训练过的 CNN 模型 MobileNet 从胸部 X 射线图像中提取特征。然后，为了确定这些特征中哪些最相关，我们应用基于人工生态系统的优化 (AEO) 算法作为特征选择器。所提出的方法应用于两个公共基准数据集（深圳和数据集 2），并允许它们实现高性能并减少计算时间。它从 MobileNet 提取的约 50,000 个特征中仅成功选择了最好的 25 个和 19 个（分别针对深圳和数据集 2）特征，同时提高了分类准确率（深圳数据集为 90.2%，数据集 2 为 94.1%）。所提出的方法优于其他深度学习方法，而与最近在这两个数据集上发表的其他作品相比，结果是最好的。同时提高分类精度（深圳数据集为 90.2%，数据集 2 为 94.1%）。所提出的方法优于其他深度学习方法，而与最近在这两个数据集上发表的其他作品相比，结果是最好的。同时提高分类精度（深圳数据集为 90.2%，数据集 2 为 94.1%）。所提出的方法优于其他深度学习方法，而与最近在这两个数据集上发表的其他作品相比，结果是最好的。