Multi-label radiography image classification has long been a topic of interest in neural networks research. In this paper, we intend to classify such images using convolution neural networks with novel localization techniques. We will use the chest x-ray images to detect thoracic diseases for this purpose. For accurate diagnosis, it is crucial to train the network with good quality images. But many chest X-ray images have irrelevant external objects like distractions created by faulty scans, electronic devices scanned next to lung region, scans inadvertently capturing bodily air etc. To address these, we propose a combination of localization and deep learning algorithms called LeDNet to predict thoracic diseases with higher accuracy. We identify and extract the lung region masks from chest x-ray images through localization. These masks are superimposed on the original X-ray images to create the mask overlay images. DenseNet-121 classification models are then used for feature selection to retrieve features of the entire chest X-ray images and the localized mask overlay images. These features are then used to predict disease classification. Our experiments involve comparing classification results obtained with original CheXpert images and mask overlay images. The comparison is demonstrated through accuracy and loss curve analyses.

利用新颖的局部化技术和卷积神经网络，我们通过胸部 X 光图像检测胸廓疾病，并且使用 Lung Disease Prediction Network（LeDNet）来提高准确性。我们通过局部化方法提取胸部 X 光图像的肺部区域面具，并将其叠加在原始 X 光图像上，形成面具叠加图像，然后使用 DenseNet-121 分类模型选择特征进行疾病分类预测。我们的实验证明了原始 CheXpert 图像和面具叠加图像的分类结果并进行了准确性和损失曲线分析。

LeDNet: 多标签放射影像分类的定位启用深度神经网络