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Numerous patients died every year due to the leading causes of deaths all over the world and burn injuries are one of them. Burn injury cases are most viewed in low and middle-income countries (LMIC). Researchers show great interest to classify the burn into different depths through digital means. In Pakistan, at provisional level, it's really a significant issue to categorize the burn and its depths due to the non-availability of expert doctors and surgeons; hence the decision for the correct first treatment can't be made, so this may cause a serious issue later on. The main objectives of this research work are to segment the burn wounds and classification of burn depths into 1st, 2nd and 3rd degrees respectively. A real-time dataset of burnt patients has been collected from the burn unit of Allied Hospital Faisalabad, Pakistan. The dataset used for this research task contains 450 images of all the three levels of burn depths. Segmentation of the burnt area was done by the use of Otsu's method of thresholding and feature vector was obtained through the use of statistical methods. We have used the Deep Convolutional Neural Network (DCNN) to estimate the burn depths. The network was trained by 65 percent of the images and the remaining 35 percent images were used for testing the accuracy of the classifier. The maximum average accuracy obtained by using the Deep Convolutional Neural Network (DCNN) classifier is reported round about 79.4% and these results are the best if we compare them with previous results. From the obtained results of this research work, non-expert doctors will be able to apply the correct first treatment for the quality evaluation of burn depths. [ABSTRACT FROM AUTHOR]

Personal health record systems (PHR) assists people to access, manage and share their own health information. PHR is getting increasingly popular these days. However, there is not even a single PHR system being deployed in Saudi Arabia considering the special needs of Saudi Arabian society. Before a PHR system is deployed, it is mandatory to know the awareness and willingness of the people so that usability of the system can be defined. In this research, we have conducted a survey in Saudi Arabia to know the level of awareness of PHR systems and the willingness of people to adopt and use such systems. In order to conduct the survey, a total of one hundred and sixty four individuals from different age groups were selected randomly keeping in perspective the target population. For reaching to the ends that stretch beyond the immediate data alone, we have used the inferential statistics for our research work. During our inferential analysis, we have selected the cross-tabulation method for comparing two different variables. The cross-tabulation of categorical and ordinal data is a good tool for summarizing demographic information and searching for blueprints. We used the Chi-Square tests to find out whether an association (or relationship) between 2 categorical variables in a sample is likely to contemplate a substantial association between these 2 variables in the population. The results indicate that the level of awareness about PHR systems is still low in the Saudi society but the people are willing to use a personalized electronic system to manage their own health. The results further indicate that the availability of the internet is a major barrier to the realization of PHR systems. These results encourage to adopt a Cloud-Dew Architecture based Layered PHR system that goes beyond the trivial network/storage/service concept to a new micro-service concept offering high scalability and availability in vertically distributed computing hierarchy. [ABSTRACT FROM AUTHOR]

The online version of the original article can be found at https://doi.org/10.1007/s11042-020-08768-y B Correction to: Multimedia Tools and Applications (2020) 79:34545-34568. b https://doi.org/10.1007/s11042-020-08768-y The original publication of this article contains incorrect spelling of the university name of the fourth affiliation. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]