Effective deep learning classification for kidney stone using axial computed tomography (CT) images.

INTRODUCTION: Stone formation in the kidneys is a common disease, and the high rate of recurrence and morbidity of the disease worries all patients with kidney stones. There are many imaging options for diagnosing and managing kidney stone disease, and CT imaging is the preferred method. OBJECTIVES: Radiologists need to manually analyse large numbers of CT slices to diagnose kidney stones, and this process is laborious and time-consuming. This study used deep automated learning (DL) algorithms to analyse kidney stones. The primary purpose of this study is to classify kidney stones accurately from CT scans using deep learning algorithms. METHODS: The Inception-V3 model was selected as a reference in this study. Pre-trained with other CNN architectures were applied to a recorded dataset of abdominal CT scans of patients with kidney stones labelled by a radiologist. The minibatch size has been modified to 7, and the initial learning rate was 0.0085. RESULTS: The performance of the eight models has been analysed with 8209 CT images recorded at the hospital for the first time. The training and test phases were processed with limited authentic recorded CT images. The outcome result of the test shows that the Inception-V3 model has a test accuracy of 98.52 % using CT images in detecting kidney stones. CONCLUSIONS: The observation is that the Inception-V3 model is successful in detecting kidney stones of small size. The performance of the Inception-V3 Model is at a high level and can be used for clinical applications. The research helps the radiologist identify kidney stones with less computational cost and disregards the need for many experts for such applications.

Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus.

This paper aims to generate a universal well-fitted mathematical model to aid global representation of the spread of the coronavirus (COVID-19) disease. The model aims to identify the importance of the measures to be taken in order to stop the spread of the virus. It describes the diffusion of the virus in normal life with and without precaution. It is a data-driven parametric dependent function, for which the parameters are extracted from the data and the exponential function derived using multiplicative calculus. The results of the proposed model are compared to real recorded data from different countries and the performance of this model is investigated using error analysis theory. We stress that all statistics, collected data, etc., included in this study were extracted from official website of the World Health Organization (WHO). Therefore, the obtained results demonstrate its applicability and efficiency.

Optimal control of a fractional order model for the COVID - 19 pandemic.

In this paper a fractional optimal control problem was formulated for the outbreak of COVID-19 using a mathematical model with fractional order derivative in the Caputo sense. The state and co-state equations were given and the best strategy to significantly reduce the spread of COVID-19 infections was found by introducing two time-dependent control measures, u 1 ( t ) (which represents the awareness campaign, lockdown, and all other measures that reduce the possibility of contacting the disease in susceptible human population) and u 2 ( t ) (which represents quarantine, monitoring and treatment of infected humans). Numerical simulations were carried out using RK-4 to show the significance of the control functions. The exposed population in susceptible population is reduced by the factor ( 1 - u 1 ( t ) ) due to the awareness and all other measures taken. Likewise, the infected population is reduced by a factor of ( 1 - u 2 ( t ) ) due to the monitoring and treatment by health professionals.