# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
## 1 Introduction  
Lung diseases are the leading health-related cause of death worldwide. There is a vital need for screening, early detection, and personalized therapies of lung cancer due to the possibility of contracting it from simple thorax illnesses. Historically, chronic obstructive pulmonary disease, emphysema, chronic bronchitis, and pneumonia have been the major causes of lung cancer [1]. Pneumonia affects approximately 450 million people, resulting in 4 million deaths per year [2]. Because it is low-cost and easily accessible, chest radiography, colloquially called chest X-ray (CXR), has become a common technique, making up nearly 45% of all radiological studies, including the diagnosis of a wealth of pathologies. An enormous number of chest radiological images have been produced globally, which are currently being analyzed through visual examinations on a slice-by-slice basis. Meanwhile, each X-ray scan can comprise dozens of patterns corresponding to hundreds of potential lung diseases, resulting in difficulty of interpretation, a high disagreement rate between radiologists, and unnecessary follow-up procedures. To deal with such a large number of data, a high degree of expertise and concentrationare required, which is time-consuming, expensive, and prone to human errors [3]. Therefore, it is important to develop adaptive algorithms for the computer-aided diagnosis (CAD) of thoracic diseases. Owing to the complexity and diversity of thorax diseases and the limited quality of CXR images, the capability of CAD is limited in the two following steps; the detection of abnormalities and their classification. In general, manual marking of pathologically abnormal regions by an expert requires even more work than labeling them. Thus, chest radiography datasets (e.g., ChestX-ray8 and ChestX-ray14 [4]) have been published, which present disease labels along with a small subset of region-level annotation of abnormal regions [4]; this annotation technique leads to the so-called weakly supervised problem during CAD. As reported in [5], the bounding boxes for localization tasks are much more informative than a single disease class label and significantly improve the model performance. Nevertheless, obtaining detailed disease localization annotations can be expensive and difficult. Therefore, designing models that are effective with a small number of annotated masks is a crucial step for the success of clinical applications. Recently, several attention-based models have been developed and proven to enhance the localization and recognition of multiple objects, despite being given only class labels during the training process [6]. Therefore, such attention mechanism can be used to address the problem of poor localization and recognition of objects in our study. In the era of computer vision, deep learning-based methods have gained state-of-the-art performances in a variety of domains, including image classification [7; 8], scene recognition [9], face recognition [10], and object detection [11]. In addition, the rapid progress of deep learning algorithms has facilitated the establishment of various annotated image datasets [12; 13; 14; 15]. These annotations indicate different characteristics important for the definition of the forthcoming challenges and subsequently possible progress. Yielding similar results in medical image analysis domains (such as human anatomical and pathological structures in radiology imaging), deep learning approaches have led to many works, including in lymph node and interstitial lung disease detection and classification [16; 17], pulmonary nodule detection in CT images [18], cerebral micro-bleed detection [19], prediction of spinal radiological scores [20], skin cancer classification [21], and automated pancreas segmentation [22]. Convolutional neural networks (CNNs), one of the most common deep learning techniques, can learn object features in an end-to-end manner considering millions of parameters in the abovementioned studies.