In diagnosing kidney stone disease, clinical specialists often apply medical imaging techniques such as CT, X‐Ray and US. Among these imaging techniques, X‐Ray is frequently chosen as the primary examination method in emergency services due to its low cost, accessibility, and low radiation levels. However, interpreting the X‐Ray images by inexperienced specialists can be challenging due to the low image quality and the presence of noise. In this study, we propose a computer‐aided diagnosis system based on deep neural networks to assist clinical specialists in detecting kidney stones using Direct Urinary System X‐Ray (DUSX) images. Firstly, in consultation with clinical specialists, we created a new dataset composed of 630 DUSX images and presented it publicly. We also defined preprocessing steps that incorporate image enhancement techniques such as GF, LoG, BF, HE, CLAHE, and CBC to enable deep neural networks to perceive the images more clearly. With these techniques, we considered the noise reduction in the DUSX images and enhanced the poor quality, especially in terms of contrast. For each preprocessing step, we created models to detect kidney stones using YOLOv4 and Mask R‐CNN architectures, which are common CNN‐based object detectors. We examined the effects of the preprocessing steps on these models. To the best of our knowledge, the combination of BF and CLAHE which is called CBC in this study, has not been applied before in the literature to enhance DUSX images. In addition, this study is the first in its field in which the YOLOv4 and Mask R‐CNN architectures have been used for the detection of kidney stones. The experimental results demonstrated the most accurate method is the YOLOv4 model, which includes the CBC preprocessing step, as the result model. This model shows that the accuracy rate, precision, recall, and F1‐score were found as 96.1%, 99.3% 96.5%, and 97.9% respectively in the test set. According to these performance metrics, we expect that the proposed model will help to reduce the unnecessary radiation exposure and associated medical costs that come with CT scans.

Abstract Microservice architecture (MSA) is a paradigm to design and develop scalable distributed applications using loosely coupled, highly cohesive components that can be deployed independently. The applications that realize the MSA may contain thousands of services that together form the overall system. Microservices interact with each other by producing and consuming data. Deploying frequently communicating services to the same physical resource would reduce network utilization, which is vital for reducing costs and improving scalability. Since the physical resources have limited capacity, it is not always possible to deploy communicating services to the same resource. Therefore, automated efficient deployment alternatives need to be generated for MSA in the design phase. To address this problem, we proposed an algorithmic approach to generate efficient microservice deployment configurations to available cloud resources in our previous study. In this study, a tool (Micro‐IDE) has been proposed to realize and evaluate this approach. The Micro‐IDE tool has been validated using a case study inspired by the Spotify application.