Browsing by Author "Malila, Bessie"

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    Open Access
    A deep learning algorithm for contour detection in synthetic 2D biplanar X-ray images of the scapula: towards improved 3D reconstruction of the scapula
    (University of Cape Town, 2020) Namayega, Catherine; Mutsvangwa, Tinashe; Malila, Bessie; Douglas, Tania
    Three-dimensional (3D) reconstruction from X-ray images using statistical shape models (SSM) provides a cost-effective way of increasing the diagnostic utility of two-dimensional (2D) X-ray images, especially in low-resource settings. The landmark-constrained model fitting approach is one way to obtain patient-specific models from a statistical model. This approach requires an accurate selection of corresponding features, usually landmarks, from the bi-planar X-ray images. However, X-ray images are 2D representations of 3D anatomy with super-positioned structures, which confounds this approach. The literature shows that detection and use of contours to locate corresponding landmarks within biplanar X-ray images can address this limitation. The aim of this research project was to train and validate a deep learning algorithm for detection the contour of a scapula in synthetic 2D bi-planar Xray images. Synthetic bi-planar X-ray images were obtained from scapula mesh samples with annotated landmarks generated from a validated SSM obtained from the Division of Biomedical Engineering, University of Cape Town. This was followed by the training of two convolutional neural network models as the first objective of the project; the first model was trained to predict the lateral (LAT) scapula image given the anterior-posterior (AP) image. The second model was trained to predict the AP image given the LAT image. The trained models had an average Dice coefficient value of 0.926 and 0.964 for the predicted LAT and AP images, respectively. However, the trained models did not generalise to the segmented real X-ray images of the scapula. The second objective was to perform landmark-constrained model fitting using the corresponding landmarks embedded in the predicted images. To achieve this objective, the 2D landmark locations were transformed into 3D coordinates using the direct linear transformation. The 3D point localization yielded average errors of (0.35, 0.64, 0.72) mm in the X, Y and Z directions, respectively, and a combined coordinate error of 1.16 mm. The reconstructed landmarks were used to reconstruct meshes that had average surface-to-surface distances of 3.22 mm and 1.72 mm for 3 and 6 landmarks, respectively. The third objective was to reconstruct the scapula mesh using matching points on the scapula contour in the bi-planar images. The average surface-to-surface distances of the reconstructed meshes with 8 matching contour points and 6 corresponding landmarks of the same meshes were 1.40 and 1.91 mm, respectively. In summary, the deep learning models were able to learn the mapping between the bi-planar images of the scapula. Increasing the number of corresponding landmarks from the bi-planar images resulted into better 3D reconstructions. However, obtaining these corresponding landmarks was non-trivial, necessitating the use of matching points selected from the scapulae contours. The results from the latter approach signal a need to explore contour matching methods to obtain more corresponding points in order to improve the scapula 3D reconstruction using landmark-constrained model fitting.
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    Open Access
    Architecture of a cognitive non-line-of-sight backhaul for 5G outdoor urban small cells
    (2017) Malila, Bessie; Falowo, Olabisi E; Ventura, Neco
    Densely deployed small cell networks will address the growing demand for broadband mobile connectivity, by increasing access network capacity and coverage. However, most potential small cell base station (SCBS) locations do not have existing telecommunication infrastructure. Providing backhaul connectivity to core networks is therefore a challenge. Millimeter wave (mmW) technologies operated at 30-90GHz are currently being considered to provide low-cost, flexible, high-capacity and reliable backhaul solutions using existing roof-mounted backhaul aggregation sites. Using intelligent mmW radio devices and massive multiple-input multiple-output (MIMO), for enabling point-to-multipoint (PtMP) operation, is considered in this research. The core aim of this research is to develop an architecture of an intelligent non-line-sight (NLOS) small cell backhaul (SCB) system based on mmW and massive MIMO technologies, and supporting intelligent algorithms to facilitate reliable NLOS street-to-rooftop NLOS SCB connectivity. In the proposed architecture, diffraction points are used as signal anchor points between backhaul radio devices. In the new architecture the integration of these technologies is considered. This involves the design of efficient artificial intelligence algorithms to enable backhaul radio devices to autonomously select suitable NLOS propagation paths, find an optimal number of links that meet the backhaul performance requirements and determine an optimal number of diffractions points capable of covering predetermined SCB locations. Throughout the thesis, a number of algorithms are developed and simulated using the MATLAB application. This thesis mainly investigates three key issues: First, a novel intelligent NLOS SCB architecture, termed the cognitive NLOS SCB (CNSCB) system is proposed to enable street-to-rooftop NLOS connectivity using predetermined diffraction points located on roof edges. Second, an algorithm to enable the autonomous creation of multiple-paths, evaluate the performance of each link and determine an optimal number of possible paths per backhaul link is developed. Third, an algorithm to determine the optimal number of diffraction points that can cover an identified SCBS location is also developed. Also, another investigated issue related to the operation of the proposed architecture is its energy efficiency, and its performance is compared to that of a point-to-point (PtP) architecture. The proposed solutions were examined using analytical models, simulations and experimental work to determine the strength of the street-to-rooftop backhaul links and their ability to meet current and future SCB requirements. The results obtained showed that reliable multiple NLOS links can be achieved using device intelligence to guide radio signals along the propagation path. Furthermore, the PtMP architecture is found to be more energy efficient than the PtP architecture. The proposed architecture and algorithms offer a novel backhaul solution for outdoor urban small cells. Finally, this research shows that traditional techniques of addressing the demand for connectivity, which consisted of improving or evolving existing solutions, may nolonger be applicable in emerging communication technologies. There is therefore need to consider new ways of solving the emerging challenges.
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    Open Access
    Image analysis for a mobile phone-based assessment of latent tuberculosis infection
    (University of Cape Town, 2020) Maclean, Sarah; Mutsvangwa, Tinashe; Malila, Bessie; Douglas, Tania
    The current, most widely used method to screen for latent tuberculosis infection is the Mantoux tuberculin skin test, where tuberculin is injected into a patient's arm and may result in a cutaneous induration forming at the site of injection. A diameter measurement of the resultant induration, recorded using a ruler and ball point pen, is currently used to indicate the presence of latent tuberculosis infection. Limitations associated with the tuberculin skin test procedure are the crudeness of the induration measurement method, the follow-up clinical visit required from patients to have their induration measured, and the need for trained clinicians who can perform the induration measurement. These limitations motivated research into a mobile phone-based screening system which can be used to obtain a more accurate measurement of the induration without the need for a second visit to the clinic by patients. The prototype screening tool consists of a user interface for capturing induration images and a backend processing system that produces a threedimensional reconstruction of the induration for measurement. Recommendations from previous studies on the prototype screening tool, which involved evaluation of the mobile application using mock induration images, included improving the accuracy of measuring the induration and evaluating the tool on real induration images. The aim of this study was to evaluate the existing backend system and explore an alternative assessment approach for assessing the induration. This was achieved through the following objectives: (1) applying the current backend system to real induration images, (2) examining the need for three-dimensional reconstruction for delineation of the induration for measurement and (3) exploring an alternative method for the assessment of induration images using deep learning. Results for the first objective showed the three-dimensional reconstruction to be unsuccessful on real images. This was due to the homogeneity between the indurations and the surrounding skin, rendering the algorithm ineffective in delineating the indurations to obtain the diameter measurement required for diagnosis. The second objective involved determining whether the image orientation or induration height affected the diagnostic measurement. It was found that real indurations are much flatter and more subtle compared to the mock indurations used in the previous studies. This motivated an alternative image assessment approach using deep learning. However, deep learning approaches require large databases of annotated images to prevent overfitting on training data. The last objective therefore involved the design and implementation of a generative adversarial network for generation of synthetic images from a limited number of real images, which allowed the generation of an unlimited number of realistic-looking synthetic images from 150 real induration images.
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    Open Access
    Implementation and Performance Evaluation of an NGN prototype using WiMax as an Access Technology
    (2012) Malila, Bessie; Ventura, Neco
    Telecommunications networks have evolved to IP-based networks, commonly known as Next Generation Networks (NGN). The biggest challenge in providing high quality realtime multimedia applications is achieving a Quality of Service (QoS) consistent with user expectations. One of the key additional factors affecting QoS is the existence of different QoS mechanisms on the heterogeneous technologies used on NGN platforms. This research investigates the techniques used to achieve consistent QoS on network technologies that use different QoS techniques. Numerous proposals for solving the end-to-end QoS problem in IP networks have adopted policy-based management, use of signalling protocols for communicating applications QoS requirements across different Network Elements and QoS provisioning in Network Elements. Such solutions are dependent on the use of traffic classification and knowledge of the QoS requirements of applications and services on the networks. This research identifies the practical difficulties involved in meeting the QoS requirements of network traffic between WiMax and an IP core network. In the work, a solution based on the concept of class-of-service mapping is proposed. In the proposed solution, QoS is implemented on the two networks and the concept of class-of-service mapping is used to integrate the two QoS systems. This essentially provides consistent QoS to applications as they traverse the two network domains and hence meet end-user QoS expectations. The work is evaluated through a NGN prototype to determine the capabilities of the networks to deliver real-time media that meets user expectations.
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    Open Access
    Security for networked smart healthcare systems: A systematic review
    (2022) Ndarhwa, Nyamwezi Perfect; Malila, Bessie; Douglas, Tania
    Background and Objectives Smart healthcare systems use technologies such as wearable devices, Internet of Medical Things and mobile internet technologies to dynamically access health information, connect patients to health professionals and health institutions, and to actively manage and respond intelligently to the medical ecosystem's needs. However, smart healthcare systems are affected by many challenges in their implementation and maintenance. Key among these are ensuring the security and privacy of patient health information. To address this challenge, several mitigation measures have been proposed and some have been implemented. Techniques that have been used include data encryption and biometric access. In addition, blockchain is an emerging security technology that is expected to address the security issues due to its distributed and decentralized architecture which is similar to that of smart healthcare systems. This study reviewed articles that identified security requirements and risks, proposed potential solutions, and explained the effectiveness of these solutions in addressing security problems in smart healthcare systems. Methods This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines and was framed using the Problem, Intervention, Comparator, and Outcome (PICO) approach to investigate and analyse the concepts of interest. However, the comparator is not applicable because this review focuses on the security measures available and in this case no comparable solutions were considered since the concept of smart healthcare systems is an emerging one and there are therefore, no existing security solutions that have been used before. The search strategy involved the identification of studies from several databases including the Cumulative Index of Nursing and Allied Health Literature (CINAL), Scopus, PubMed, Web of Science, Medline, Excerpta Medical database (EMBASE), Ebscohost and the Cochrane Library for articles that focused on the security for smart healthcare systems. The selection process involved removing duplicate studies, and excluding studies after reading the titles, abstracts, and full texts. Studies whose records could not be retrieved using a predefined selection criterion for inclusion and exclusion were excluded. The remaining articles were then screened for eligibility. A data extraction form was used to capture details of the screened studies after reading the full text. Of the searched databases, only three yielded results when the search strategy was applied, i.e., Scopus, Web of science and Medline, giving a total of 1742 articles. 436 duplicate studies were removed. Of the remaining articles, 801 were excluded after reading the title, after which 342 after were excluded after reading the abstract, leaving 163, of which 4 studies could not be retrieved. 159 articles were therefore screened for eligibility after reading the full text. Of these, 14 studies were included for detailed review using the formulated research questions and the PICO framework. Each of the 14 included articles presented a description of a smart healthcare system and identified the security requirements, risks and solutions to mitigate the risks. Each article also summarized the effectiveness of the proposed security solution. Results The key security requirements reported were data confidentiality, integrity and availability of data within the system, with authorisation and authentication used to support these key security requirements. The identified security risks include loss of data confidentiality due to eavesdropping in wireless communication mediums, authentication vulnerabilities in user devices and storage servers, data fabrication and message modification attacks during transmission as well as while the data is at rest in databases and other storage devices. The proposed mitigation measures included the use of biometric accessing devices; data encryption for protecting the confidentiality and integrity of data; blockchain technology to address confidentiality, integrity, and availability of data; network slicing techniques to provide isolation of patient health data in 5G mobile systems; and multi-factor authentication when accessing IoT devices, servers, and other components of the smart healthcare systems. The effectiveness of the proposed solutions was demonstrated through their ability to provide a high level of data security in smart healthcare systems. For example, proposed encryption algorithms demonstrated better energy efficiency, and improved operational speed; reduced computational overhead, better scalability, efficiency in data processing, and better ease of deployment. Conclusion This systematic review has shown that the use of blockchain technology, biometrics (fingerprints), data encryption techniques, multifactor authentication and network slicing in the case of 5G smart healthcare systems has the potential to alleviate possible security risks in smart healthcare systems. The benefits of these solutions include a high level of security and privacy for Electronic Health Records (EHRs) systems; improved speed of data transaction without the need for a decentralized third party, enabled by the use of blockchain. However, the proposed solutions do not address data protection in cases where an intruder has already accessed the system. This may be potential avenues for further research and inquiry.
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    Open Access
    User-interface design and evaluation in a mobile application for detecting latent tuberculosis
    (2019) Farao, Jaydon Ethan; Douglas, Tania Samantha; Malila, Bessie; Mutsvangwa,Tinashe
    Treatment and monitoring of tuberculosis have been met with various interventions to reduce its prevalence. One such intervention, to detect and prevent latent tuberculosis infection (LTBI), is the tuberculin skin test (TST), for which an induration response on a patient’s arm is an indication of LTBI. The test requires the patient to return to a clinic 48 to 72 hours after TST administration for assessment of the response. This is a challenge because of financial and accessibility obstacles, especially in under-resourced regions. A mobile health (mHealth) application (app) has been developed for remote assessment of the response to the TST. The previous version of the LTBI screening app, however, had usability limitations. The app is intended for use by patients and healthcare workers; thus, ease of use is important. There is a lack of literature on the usability of mHealth apps, especially in under-resourced settings. In this project, the user interface of the app was redesigned and tested. The Information Systems Research (ISR) framework was integrated with design thinking for this purpose. The project included creating mock-ups of the interface which were iteratively prototyped with ten student participants, adjusted, and assessed according to the user feedback. Thereafter, the Android Studio software was used to adjust the user interface based on the insights gained through the progression of prototypes. The usability of the updated app was tested and assessed with ten healthcare workers at a community health clinic in Khayelitsha in Cape Town, South Africa. Data collection and analysis comprised both qualitative and quantitative methods. Observations, the “think aloud” approach, and the post-study system usability questionnaire were used for data collection. Student participants highlighted various usability limitations of the app during each iteration. The major usability limitations included: the complex image capture protocol, misunderstanding of instructions, and time taken to capture images. Engagement with students allowed for improvement of the app interface and enabled adequate preparation for testing in the field with end-users. Furthermore, improving the app interface before engaging with healthcare workers, enabled context specific limitations that would affect the usability of the app, to be explored during the field testing. These included safety concerns when using the app and the privacy of health information. Future work should explore how these concerns, as well as other social factors, affect usability. Furthermore, improving the image capture protocol is required for improving the usability of the app.