A machine learning-based system for detecting leishmaniasis in microscopic images.

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.

Cross layer design for optimised region of interest of ultrasound video data over mobile WiMAX.

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.

Cross-layer ultrasound video streaming over mobile WiMAX and HSUPA networks.

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.

Dynamic subframe allocation for mobile broadband m-health using IEEE 802.16j mobile multihop relay networks.

The concept of 4G health will be one of the key focus areas of future m-health research and enterprise activities in the coming years. WiMAX technology is one of the constituent 4G wireless technologies that provides broadband wireless access (BWA). Despite the fact that WiMAX is able to provide a high data rate in a relatively large coverage; this technology has specific limitations such as: coverage, signal attenuation problems due to shadowing or path loss, and limited available spectrum. The IEEE 802.16j mobile multihop relay (MMR) technology is a pragmatic solution designed to overcome these limitations. The aim of IEEE 802.16j MMR is to expand the IEEE 802.16e's capabilities with multihop features. In particular, the uplink (UL) and downlink (DL) subframe allocation in WiMAX network is usually fixed. However, dynamic frame allocation is a useful mechanism to optimize uplink and downlink subframe size dynamically based on the traffic conditions through real-time traffic monitoring. This particular mechanism is important for future WiMAX based m-health applications as it allows the tradeoff in both UL and DL channels. In this paper, we address the dynamic frame allocation issue in IEEE 802.16j MMR network for m-health applications. A comparative performance analysis of the proposed approach is validated using the OPNET Modeler(®). The simulation results have shown an improved performance of resource allocation and end-to-end delay performance for typical medical video streaming application.

Mapping of multiple parameter m-health scenarios to mobile WiMAX QoS variables.

Multiparameter m-health scenarios with bandwidth demanding requirements will be one of key applications in future 4 G mobile communication systems. These applications will potentially require specific spectrum allocations with higher quality of service requirements. Furthermore, one of the key 4 G technologies targeting m-health will be medical applications based on WiMAX systems. Hence, it is timely to evaluate such multiple parametric m-health scenarios over mobile WiMAX networks. In this paper, we address the preliminary performance analysis of mobile WiMAX network for multiparametric telemedical scenarios. In particular, we map the medical QoS to typical WiMAX QoS parameters to optimise the performance of these parameters in typical m-health scenario. Preliminary performance analyses of the proposed multiparametric scenarios are evaluated to provide essential information for future medical QoS requirements and constraints in these telemedical network environments.

Performance analysis of medical video streaming over mobile WiMAX.

Wireless medical ultrasound streaming is considered one of the emerging application within the broadband mobile healthcare domain. These applications are considered as bandwidth demanding services that required high data rates with acceptable diagnostic quality of the transmitted medical images. In this paper, we present the performance analysis of a medical ultrasound video streaming acquired via special robotic ultrasonography system over emulated WiMAX wireless network. The experimental set-up of this application is described together with the performance of the relevant medical quality of service (m-QoS) metrics.