Annabella Habinka Dorothy Basaza | Makerere University Kampala (original) (raw)

Papers by Annabella Habinka Dorothy Basaza

Research Square (Research Square), Jun 20, 2024

Research Square (Research Square), Jun 19, 2024

European Journal of Health Sciences, 2021

Aims: Low and middle-income countries are still facing challenges of dysfunctional referral syste... more Aims: Low and middle-income countries are still facing challenges of dysfunctional referral systems which have impaired health service provision. This review aimed at investigating these challenges to understand their nature, cause, and the impacts they have on health service provision. Methods: Database search was made in Google scholar, ACM Library, PubMed health, and BMC public health, and a total of 123 papers were generated. Only 14 fitted the inclusion criteria. Inclusion criteria included studies that were both quantitative and qualitative addressing challenges facing referral systems or health referral systems, studies describing the barriers to effective referral systems, and studies describing factors that affect referral systems. The review only included studies conducted in LMICs and included literature between January 2010 and February 2021. Findings: Results revealed that human resource and financial constraints, non-compliance, and communication are the key challenges...

2014 IST-Africa Conference Proceedings, 2014

Kabarungi M, 2023

In Uganda, blended learning (BL) has emerged as a popular approach to education, particularly in ... more In Uganda, blended learning (BL) has emerged as a popular approach to education, particularly in response to the COVID-19 pandemic, this has forced educational institutions to adopt remote learning models. Complex adaptive and Community of inquiry frameworks have been implemented worldwide to support the adoption of BL. However, the existing blended learning frameworks (BLFs) in Uganda like any other third world country suffer from issues related to policies, training, support and infrastructure. As a solution to the above challenges, a suitable BLF for Higher Educational Institutions of Learning (HEIL) is required. However designing a BLF requires careful consideration of a range of factors to ensure the optimal learning experience for students, thus this study aimed at exploring the requirements for designing a BLF. A cross-sectional survey was conducted in three universities in south western Uganda by the help of a pretested questionnaire which was given to 1495 participants who met the inclusion criteria and consented to participate in the study. Quantitative data was collected and analyzed using IBM SPSS-26. The results revealed that 99% of the respondents strongly agreed that there are no BLFs in their institutions hence need for designing one. However, 7.19% of the respondents were not sure whether they needed a BLF. Furthermore, 94.6% of the respondents strongly agreed that BLF to be designed should align with the objectives of their institutions as one of the requirements to be considered before designing it. Furthermore, 95.9%, 98.8% and 93.8% of the respondents strongly agreed that university policies, training and support for creation of e-content respectively should be highly considered. Therefore, basing on these findings, this research study developed a Requirement Specification Document that can be used as an input for designing a BLF that will enhance the adoption of BL in HEIL in Uganda.

Campbell et al., 2023, 2023

Background: The expansion of cellular phones in sub-Saharan Africa spurred the development of SMS... more Background: The expansion of cellular phones in sub-Saharan Africa spurred the development of SMS text message-based mobile health (mHealth) technology. Numerous SMS text message-based interventions have attempted to increase retention in care for people living with HIV in sub-Saharan Africa. Many of these interventions have failed to scale. Understanding theory-grounded factors leading to mHealth acceptability is needed to create scalable, contextually appropriate, and user-focused interventions to improve longitudinal HIV care for people living with HIV in sub-Saharan Africa. Objective: In this study, we aimed to understand the relationship between constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT), constructs identified in previous qualitative research, and behavioral intention to use a novel SMS text message-based mHealth intervention designed to improve care retention among people living with HIV initiating treatment in rural Uganda.

2019 IST-Africa Week Conference (IST-Africa)

Globally, cervical cancer ranks as the fourth most prevalent cancer affecting women. However, cer... more Globally, cervical cancer ranks as the fourth most prevalent cancer affecting women. However, cervical cancer can be treated if detected at an early stage. Pap-smear is a good tool for screening of cervical cancer but the manual analysis is error-prone, tedious and time-consuming. The objective of this study was to rule out these limitations by automating the process of cervical cancer classification from pap-smear images by using an enhanced fuzzy c-means algorithm. Simulated annealing coupled with a wrapper filter was used for feature selection. The evaluation results showed that our method outperforms many of previous algorithms in classification accuracy (99.35%), specificity (97.93%) and sensitivity (99.85%), when applied to the Herlev benchmark pap-smear dataset. The overall accuracy, sensitivity and specificity of the classifier on prepared pap-smear slides was 95.00%, 100% and 90.00% respectively. False Negative Rate (FNR), False Positive Rate (FPR) and classification error of 0.00%, 10.00% and 5.00% respectively were obtained. The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed tool has the capability of analyzing 1-2 smears per minute as opposed to the 5-10 minutes per slide in the manual analysis.

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Cervical cancer ranks as the fourth most prevalent cancer affecting women worldwide and its early... more Cervical cancer ranks as the fourth most prevalent cancer affecting women worldwide and its early detection provides the opportunity to help save life. Automated diagnosis of cervical cancer from pap-smear images enables accurate, reliable and timely analysis of the condition’s progress. Cell segmentation is a fundamental aspect of successful automated pap-smear analysis. In this paper, a potent approach for segmentation of cervical cells from a pap-smear image into the nucleus, cytoplasm and background using pixel level information is proposed. A number of pixels from the nuclei, cytoplasm and background are extracted from 100 images to form a feature vector which is trained using noise reduction, edge detection and texture filters to produce a pixel level classifier. Comparison of the segmented images’ nucleus and cytoplasm parameters (nucleus area, longest diameter, roundness, perimeter and cytoplasm area, longest diameter, roundness, perimeter) with the ground truth image features yielded average percentage errors of 0.14, 0.28, 0.03, 0.30, 0.15, 0.25, 0.05 and 0.39 respectively. Validation of the pixel classifier with 10fold cross-validation yielded pixel classification accuracy of 98.50%, 97.70% and 98.30% with Fast Random Forest, Naïve Bayes and J48 classification methods respectively. Comparison of the segmented nucleus and cytoplasm with the ground truth nucleus and cytoplasm segmentations resulted into a Zijdenbos similarity index greater than 0.9321 and 0.9639 for nucleus and cytoplasm segmentation respectively. The results indicated that the proposed pixel level segmentation classifier was able to extract the nucleus and cytoplasm regions accurately and worked well even though there was no significant contrast between the components in the image. The results from cross-validation and test set evaluation imply that the classifier can segment cells outside the training dataset with high precision. Choosing an appropriate feature vector for training the classifier was a great challenge and a novel task in the proposed approach. As a result, good segmentation of the nucleus and cytoplasm was attained. Given the accuracy of the classifier in segmenting the nucleus, which plays an important role in cervical cancer diagnosis, the classifier can be adopted in systems for automated diagnosis of cervical cancer from pap-smear images.

International Journal of Computer Applications, 2021

Purpose: The study aimed at establishing the contextual factors affecting performance of mobile s... more Purpose: The study aimed at establishing the contextual factors affecting performance of mobile services for monitoring delivery of public health services in Uganda. Methodology: The study used a qualitative research design in an interpretivist paradigm where the identified factors were subjected to analysis using documentary evidence and qualitative data from interviews. Using purposive sampling, six case studies among institutions responsible for monitoring health service delivery in Uganda were selected. Data was categorized through creating code families, grouping codes with similar attributes into broad categories and represent a higher order grouping of data from which the researcher began to build conceptual model and categories continued until saturation point. Findings: It was established that lack of power for charging mobile devices, limited content and coverage of data captured by mobile technologies, limited man power, knowledge and skills of using mobile technologies a...

Background: Digital pathology and microscopy image analysis is widely used for comprehensive stud... more Background: Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology especially for cervical cancer screening from pap-smears. Manual assessment of pap-smears is labour intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. A critical prerequisite in automated analysis of pap-smears is nucleus and cytoplasm segmentation, which is the basis of cervical cancer screening. This paper articulates a potent approach to the segmentation of cervical cells into nucleus and cytoplasm using a quadtree decomposition approach with statistical measures.Results: Choosing an appropriate quadtree decomposition strategy was a great challenge and a novel task in the proposed approach. The image is pre-processed using an enhanced median filter and is decomposed based on the mean, maximum entropy and the var...

Journal of Data Analysis and Information Processing, 2020

International Journal of New Technology and Research, 2020

International Journal of New Technology and Research, 2018

International Journal of Computer Trends and Technology, 2018

BioMedical Engineering OnLine, 2019

Computer methods and programs in biomedicine, 2018

Early diagnosis and classification of a cancer type can help facilitate the subsequent clinical m... more Early diagnosis and classification of a cancer type can help facilitate the subsequent clinical management of the patient. Cervical cancer ranks as the fourth most prevalent cancer affecting women worldwide and its early detection provides the opportunity to help save life. To that end, automated diagnosis and classification of cervical cancer from pap-smear images has become a necessity as it enables accurate, reliable and timely analysis of the condition's progress. This paper presents an overview of the state of the art as articulated in prominent recent publications focusing on automated detection of cervical cancer from pap-smear images. The survey reviews publications on applications of image analysis and machine learning in automated diagnosis and classification of cervical cancer from pap-smear images spanning 15 years. The survey reviews 30 journal papers obtained electronically through four scientific databases (Google Scholar, Scopus, IEEE and Science Direct) searched...

2018 IST-Africa Week Conference (IST-Africa), 2018

Cervical cancer ranks as the fourth most prevalent form of cancer affecting women worldwide and i... more Cervical cancer ranks as the fourth most prevalent form of cancer affecting women worldwide and its early detection provides the opportunity to help save life. Automated diagnosis and classification of cervical cancer has become a necessity as it enables accurate, reliable and timely analysis of the condition’s progress. This survey paper presents an overview of the state of the art as articulated in a number of prominent recent publications focusing on automated diagnosis and classification of cervical cancer from pap-smear images. It reviews thirty journal papers obtained electronically through four scientific databases searched using three sets of keywords: (1) Segmentation, Classification, Cervical Cancer; (2) Medical Imaging, Machine Learning, pap-smear Images; (3) Automated, Segmentation, Pap-smear Images. The review found that some techniques are used more frequently than others are: for example, filtering, thresholding and KNN are the most used techniques for preprocessing, ...