# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
## 2 Related Works  
Deep learning techniques have led to profound breakthroughs in various computer vision applications, such as the classification of natural and medical images [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. This success has prompted many researchers to adopt deep CNNs for the diagnosis of thoracic diseases on CXR images. TUNA-Net [36] presented unsupervised domain adaption for classifying pneumonia from normal patients and achieved an AUC of 96.3%. Moreover, a CNN with attention feedback (CONAF) was presented by the authors of [37]. They first extracted saliency maps from a repository of over 430,000 X-ray images to see if localization errors occurred and back-propagated when necessary. Then, a recurrent attention model learned to observe a short sequence of smaller image portions for further improvements of abnormal regions localization. In addition, generative adversarial networks (GANs) [38] were applied to create artificial images based on a modestly sized label dataset. Then, a CNN was trained to detect pathologies among five classes from CXR images with a substantial improvement in classification. There have been many studies attempting to achieve outstanding results of both localization and classification tasks using deep learning applied to the ChestX-ray14 dataset. A unified weakly supervised multilabel classification framework was proposed in [4]. Their objective was first to check if one or more pathologies were present in each X-ray image of the ChestX-ray8 dataset and then localize them using the activation and weights extracted from the DCNN. After employing different pretrained models, e.g., the AlexNet [7], GoogleNet [39], VGGNet-16 [8], and ResNet-50 [40], disregarding the fully connected layers and the soft-max classification layers, they inserted a transition layer, a global pooling layer, a prediction layer, and a loss layer. This allowed the combination of deep activations from the transition layer and the weights of the prediction inner-product layer, enabling them to find the plausible spatial locations. Because there can be multiple pathological patterns on each chest radiograph, the authors developed an approach to further leverage the interdependences among target labels in 14 pathological pattern predictions and achieved better performances in [41]. They verified, without pretraining and a carefully designed LSTM baseline model that ignored the label dependences, that they outperformed the pretrained model by a large margin. Similarly, the authors experimented with a set of deep learning models and presented a cascaded deep neural network that performed better than the baseline model, which used transfer learning when dealing with imbalanced-14-pathologies diagnosis in [42]. Their proposed approach could model the complex dependency between class labels and could generate the training strategy of boosting methods by considering the loss functions. In addition, the authors designed CheXNet [43], which is a 121-layer CNN using dense connections and batch normalization that feeds input CXR images to the model and outputs the probability of pneumonia, along with a heat-map that localized the most indicative features of pneumonia in the image. They found that their model exceeded both the average radiologist performances on pneumonia detection and the best published results on all 14 diseases at that time. More recently, different deep learning-based methods have been developed to tackle the ChestX-ray14 problem. The authors proposed the ChestNet model to address the effective diagnosis of 14 thorax diseases in [44]. Their model consisted of two main branches: the first was a classification branch, which served as a unified feature extraction network pretrained with the ResNet-152 model [40] to escape the complexity of handling with handcrafted features; the second was an attention branch, which explored the correlation between class labels, allowing the model to find the locations of abnormal regions.