## 4 Experimental Methodology
### Datasets  
The compared classification algorithms were evaluated utilizing the chest X-ray (Pneumonia) dataset, the Shenzhen lung mask (Tuberculosis) dataset and the CT Medical images dataset.  
* _Chest X-ray (Pneumonia) dataset_: The dataset contains 5830 chest X-ray images (anterior-posterior) which were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children's Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients' routine clinical care. For the analysis of chest X-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the artificial intelligence system. In order to account for any grading errors, the evaluation set was also checked by a third expert. The dataset was partitioned into two sets (training/testing). The training set consisting of 5216 examples (1341 normal, 3875 pneumonia) and the testing set with 624 examples (234 normal, 390 pneumonia) as in [28].
* _Shenzhen lung mask (Tuberculosis) dataset_: Shenzhen Hospital is one of the largest hospitals in China for infectious diseases with a focus both on their prevention, as well as treatment. The X-rays were collected within a one-month period, mostly in September 2012, as a part of the daily routine, using a Philips DR Digital Diagnost system. The dataset was constructed by manually-segmented lung masks for the Shenzhen Hospital X-ray set as presented in [29]. These segmented lung masks were originally utilized for the description of the lung segmentation technique in combination with lossless and lossy data augmentation. The segmentation masks for the Shenzhen Hospital X-ray set were manually prepared by students and teachers of the Computer Engineering Department, Faculty of Informatics and Computer Engineering, National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\" [29]. The set contained 279 normal CXRs and 287 abnormal ones with tuberculosis. All classification algorithms were evaluated using the stratified ten-fold cross-validation.
* _CT Medical images dataset_: This data collectioncontains 100 images [30] which constitute part of a much larger effort, focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from _the cancer genome Atlas_[31]. The images consist of the middle slice of all Computed Tomography (CT) images taken from 69 different patients. The dataset is designed to allow different methods to be evaluated for examining the trends in CT image data associated with using contrast and patient age. The basic idea is to identify image textures, statistical patterns and features correlating strongly with these traits and possibly build simple tools for automatically classifying these images when they have been misclassified (or finding outliers which could be suspicious cases, bad measurements, or poorly calibrated machines). All classification algorithms were evaluated using the stratified ten-fold cross-validation.  
The training partition was randomly divided into labeled and unlabeled subsets. In order to study the influence of the amount of labeled data, four different ratios (\(R\)) of the training data were used: 10%, 20%, 30% and 40%. Using the recommendation established in [9; 32] in the division processwe do not maintain the class proportion in the labeled and unlabeled sets since the main aim of semi-supervised classification is to exploit unlabeled data for better classification results. Hence, we use a random selection of examples that will be marked as labeled instances, and the class label of the rest of the instances will be removed. Furthermore, we ensure that every class has at least one representative instance.