Early detection of pneumoconiosis in X-Rays has been a challenging task that leads to high inter- and intra-reader variability. Motivated by the success of deep learning in general and medical image classification, this paper proposes an approach to automatically detect pneumoconiosis using a deep feature based binary classifier. The features are extracted from X-rays using deep transfer learning, comprising both low and high-level feature sets. For this, a CNN model pre-trained with a transfer learning from a CheXNet model was initially used to extract deep features from the X-Ray images, then the deep features were mapped to higher-dimensional feature spaces for classification using Support Vector Machine (SVM) and CNN based feature aggregation methods. In order to cross validate the proposed method, the training and testing images were randomly split into three folds before each experiment. Nine evaluation metrics were employed to compare the performance of the proposed method and state-of-the-art methods from the literature that used the same datasets. The experimental results show that the proposed framework outperformed others, achieving an accuracy of 92.68% in the automated detection of pneumoconiosis.

在X射线中早期发现尘肺病一直是一项艰巨的任务，它会导致阅读器之间和阅读器内部的差异很大。受到普通和医学图像分类中深度学习成功的推动，本文提出了一种使用基于深度特征的二元分类器自动检测尘肺的方法。使用包括低级和高级特征集在内的深度转移学习从X射线中提取特征。为此，首先使用从CheXNet模型进行转移学习预训练的CNN模型从X射线图像中提取深层特征，然后将深层特征映射到高维特征空间以使用支持向量机（ SVM）和基于CNN的特征聚合方法。为了交叉验证提出的方法，在每次实验之前，将训练和测试图像随机分为三部分。使用九种评估指标来比较所提出的方法和使用相同数据集的文献所提供的最新方法的性能。实验结果表明，所提出的框架优于其他框架，在尘肺的自动检测中达到了92.68％的准确性。