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.

At the beginning of 2020, the new coronavirus disease (Covid‐19), a deadly viral illness, is declared as a public health emergency situation by WHO. Consequently, it is accepted as pandemic that affected millions of people worldwide. Italy is one of the most affected countries by Covid‐19 disease among the world. In this article, our main goal is to investigate the effect of intensity of Covid‐19 cases based on the population size and tourism factors in certain regions of Italy by visual data analysis. The regions of Lombardia, Veneto, Campania, Emilia‐Romagna, Piemonte are the top five regions covering 58.50% of the total Covid‐19 cases diagnosed in Italy. It has been shown by visual data analysis that population and tourism factors play an important role in the spread of Covid‐19 cases in these five regions. In addition, a prediction model was created using Bi‐LSTM and ARIMA algorithms to forecast the number of Covid‐19 cases occurring in these five regions in order to take early action. We can conclude that these northern regions have been affected mostly by Covid‐19 and the distribution of the resident population and tourist flow factors affected the number of Covid‐19 cases in Italy.