It is well known that the evolution of 4G-based mobile multimedia network systems will contribute significantly to future mobile healthcare (m-health) applications that require high bandwidth and fast data rates. Central to the success of such emerging applications is the compatibility of broadband networks, such as mobile Worldwide Interoperability For Microwave Access (WiMAX) and High-Speed Uplink Packet Access (HSUPA), and especially their rate adaption issues combined with the acceptable real-time medical quality of service requirements. In this paper, we address the relevant challenges of cross-layer design requirements for real-time rate adaptation of ultrasound video streaming in mobile WiMAX and HSUPA networks. A comparative performance analysis of such approach is validated in two experimental m-health test bed systems for both mobile WiMAX and HSUPA networks. The experimental results have shown an improved performance of mobile WiMAX compared to the HSUPA using the same cross-layer optimization approach.

The application of advanced error concealment techniques applied as a post-process to conceal lost video information in error-prone channels, such as the wireless channel, demand additional processing at the receiver. This increases the delivery delay and needs more computational power. However, in general, only a small region within medical video is of interest to the physician and thus if only this area is considered, the number of computations can be curtailed. In this paper we present a technique whereby the Region of Interest (ROI) specified by the physician is used to delimit the area where the more complex concealment techniques are applied. A cross layer design approach in mobile WiMAX wireless communication environment is adopted in this paper to provide an optimized Quality of Experience (QoE) in the region that matters most to the mobile physician while relaxing the requirements in the background, ensuring real-time delivery. Results show that a diagnostically acceptable Peak Signal-to-Noise-Ratio (PSNR) of about 36 dB can still be achieved within reasonable decoding time.

Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

No abstract