Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in metric learning to address this problem. In recent years, deep metric learning, which provides a better solution for nonlinear data through activation functions, has attracted researchers' attention in many different areas. This article aims to reveal the importance of deep metric learning and the problems dealt with in this field in the light of recent studies. As far as the research conducted in this field are concerned, most existing studies that are inspired by Siamese and Triplet networks are commonly used to correlate among samples while using shared weights in deep metric learning. The success of these networks is based on their capacity to understand the similarity relationship among samples. Moreover, sampling strategy, appropriate distance metric, and the structure of the network are the challenging factors for researchers to improve the performance of the network model. This article is considered to be important, as it is the first comprehensive study in which these factors are systematically analyzed and evaluated as a whole and supported by comparing the quantitative results of the methods.

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Declared a pandemic disease, COVID-19 has affected the lives of millions of people and had significant effects on public health. Despite the development of effective vaccines against the COVID-19 virus, COVID-19 case rates continue to increase worldwide. According to studies in the literature, artificial intelligence methods are used effectively for the detection of the COVID-19. Especially deep learning-based approaches have achieved very successful results in clinical diagnostic studies and other fields. In this study, a new approach using X-ray images is proposed to detect COVID-19 disease. In the proposed method, the Angle transform (AT) method is first applied to the X-ray images. The AT method proposed in this study is an important novelty in the literature, and there is no such approach in previous studies. This transformation uses the angle information created by each pixel on the image with the surrounding pixels. Using the angle transform approach, eight different images are obtained for each image in the dataset. These images are trained with a hybrid deep learning model, which combines GoogleNet and LSTM models, and COVID-19 disease detection is carried out. A dataset from the Mendeley database is used to test the proposed approach. High classification accuracy of 98.97% is achieved with the AT+GoogleNet+LSTM approach. The results obtained were also compared with other studies in the literature. The presented results reveal that the proposed method is successful for COVID-19 detection using chest X-ray images. Direct transfer methods were also applied to the data set used in the study. However, low success results were observed according to the proposed approach. The proposed approach has the flexibility to be applied effectively to different medical images

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