# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 1 Introduction  
Recent years have seen an increase in the digitalization of health records as well as a gradual increase in computational power, enabling the use of machine learning and deep learning algorithms in various areas of medicine such as diagnosis, outcome prediction, and clinical decision-making [1, 2]. Medical images are a major component of patients' electronic health records, making medical image analysis an active field of research for machine learning and deep learning [2]. Currently, the extraction of information from medical images is performed manually by radiologists and other professionals in the healthcare field, making the process dependent on the individual's experience and knowledge, vulnerable to human error, and time-consuming. In order to address these issues, extensive research has been performed on the usage of machine learning and deep learning in medical image analysis to achieve more efficient and precise results [3]. The tasks studied in the context of medical image analysis include segmentation, detection, de-noising, reconstruction, and classification [4]. This survey paper focuses on recent studies in image classification only. Image classification involves the assignment of one or more labels to an image based on the features extracted from it. The process consists of various steps, including preprocessing, feature extraction, feature selection, and classification. The extracted features can be low-level such as color, shape, intensity, texture, boundary, and position information, as well as high-level such as bag-of-words, scale-invariant feature transform, and fisher vector [5, 6]. In traditional machine learning methods, features are generally extracted separately and then fed into the classification model. On the other hand, deep learning algorithms allow the feature extraction and classification steps to be combined in one network [5]. In addition, deep learning models apply end-to-end learning, in which the model receives a dataset of labeled images and extracts descriptive, hierarchical, and highly representative features with respect to each label which the model then uses for the classification task [5, 7]. Deep learning methods can combine complex and low-level features and are less vulnerable to human errors [6]. Research has shown that deep learning models frequently outperformtraditional machine learning algorithms in image classification tasks [5; 8; 9; 10]. However, deep learning methods have their own limitations, such as the requirement for more time and higher computing power. Furthermore, traditional machine learning methods may achieve better results on smaller datasets [11; 12; 13]. Medical image classification plays an essential role in the analysis and diagnosis of diseases as well as clinical care and treatment. Medical images can be of different modalities based on the biomedical devices used to generate them. These modalities include Projectional Radiography (X-rays), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound Imaging [14]. X-ray is the most common medical imaging technique in which electromagnetic waves are used to create a two-dimensional representation of the body's internal structure based on the varying rates of wave absorption in tissues with different densities. CT is a computerized procedure used to generate cross-sectional X-ray images, which can be combined to create a three-dimensional image, providing a more detailed representation of the region compared to conventional X-rays [15]. MRI generates three-dimensional anatomical images based on the behavior of the atomic nuclei in response to powerful magnetic fields and radiofrequency pulses [16]. Ultrasound imaging uses high-frequency sound waves to generate two-dimensional images of organs and tissues based on the reflection of the sound waves from different body structures [17]. In addition to these, dermoscopic images are also often used for medical image classification. These are two-dimensional images visualizing the subsurface skin structures.