## 2 Related Work  
The significance of medical imaging for the diagnosis of diseases has been established for the treatment of chest pathologies and their early detection. During the last decades, the advances of digital technology and chest radiography as well as the rapid development of digital image retrieval have renewed the progress in new technologies for the diagnosis of lung abnormalities. More specifically, research has been focused on the development of Computer-Aided Diagnostic (CAD) models for abnormality detection in order to assist medical staff. Along this line, a variety of methodologies have been proposed based on machine learning techniques, aiming on classifying and/or detecting abnormalities in patients' medical images. A number of studies have been carried out in recent years; some useful outcomes of them are briefly presented below. [PERSON] et al. [19] proposed a CAD system for tuberculosis in conventional posteroanterior chest radiographs. Their proposed model initially utilizes a graph cut segmentation method to extract the lung region from the CXRs and then a set of texture and shape features in the lung region is computed in order to classify the patient as normal or abnormal. Their extensive numerical experiments on two real-world datasets illustrated the efficiency of the proposed CAD system for tuberculosis screening, achieving higher performance compared to that of human readings. [PERSON] et al. [20] recommend a novel CAD system for detecting tuberculosis on chest X-rays based on multiple-instance learning. Their proposed system is based on the idea of utilizing probability estimations, instead of the sign of a decision function, to guide the multiple-instance learning process. Furthermore, an advantage of their method is that it does not require labeling of each feature sample during the training process but only a global class label characterizing a group of samples. [PERSON] et al. [21] utilized a multi-class support vector machine classifier and developed an efficient lung cancer detection and prediction model. The image enhancement and image segmentation have been done independently in every stage of the classification process. Image scaling, color space transformation and contrast enhancement have been utilized for image enhancement while threshold and marker-controlled watershed have been utilized for segmentation. In the sequel, the support vector machine classifier categorizes a set of textural features extracted from the separated regions of interest. Based on their numerical experiments, the authors concluded that the proposed algorithm can efficiently detect a cancer-affected cell and its corresponding stage such as initial, middle, or final. Furthermore, in case no cancer-affected cell is found in the input image then it checks the probability of lung cancer. In more recent works, [PERSON] [22] focused on the detection of abnormalities in chest X-ray images, having available only a fairly small size dataset of annotated images. Their proposed method deals with both problems of labeled data scarcity and data domain overfitting, by utilizing Generative Adversarial Networks (GAN) in a SSL architecture. In general, GAN utilize two networks: a generator which seeks to create as realistic images as possible and a discriminator which seeks to distinguish between real data and generated data. Next, these networks are involved in a minimax game to find the Nash equilibrium between them. Based on their experiments, the author concluded that the annotation effort is reduced considerably to achieve similar performance through supervised training techniques. In [2], [PERSON] et al. evaluated the classification efficacy of an ensemble SSL algorithm, called CST-Voting, for CXR classification of tuberculosis. The proposed algorithm combines the individual predictions of three efficient self-labeled algorithms i.e., Co-training, Self-training and Tri-training using a simple majority voting methodology.