On this page, we present the projects that we have undertaken under the following fields: Agriculture, Health, Natural Language Processing and Neuromorphic Computing. Follow the project website for more details.


A Portable Deep Learning-based Platform for Passion Fruit Disease Identification (Katunda Project)

Pests and diseases pose a key challenge to passion fruit farmers across the country. They lead to loss of investment as yields reduce and losses increases. As the majority of the farmers, including passion fruit farmers, in the country are smallholder farmers from low-income households, they do not have sufficient information and means to combat these challenges. Without the required knowledge about the health of their crops, farmers cannot intervene promptly to turn the situation around. This project addresses the problem of lack of a reliable, timely diagnostic platform for passion fruit diseases, proposing to develop a low-cost hand-held diagnostic device (based on low compute devices, specifically the raspberry) making use of state-of-the-art machine learning techniques for identification.

A Katumba, M Bomera, C Mwikirize, G Namulondo

Development of Machine Learning Datasets for Crop Pest and Disease Diagnosis based on Crop Imagery and Spectrometry Data

The project aims to deliver open, accessible and quality machine learning datasets for crop pests and disease diagnosis based on crop imagery and spectrometry data from Uganda, Tanzania, Namibia and Ghana.

A Katumba, Makerere AI Research Lab


Machine Learning-guided Screening of COVID-19 using Point-of-Care Ultrasound in Uganda

In the absence of a cure or vaccine for the novel coronavirus, early diagnosis and isolation of COVID-19 patients is critical to stemming community spread. Ideally, this would involve mass and regular testing of all persons, for which Uganda does not yet have the capacity. Therefore, testing resources have been focused on high-risk demographics and their contacts. Screening toward targeted testing of the general population is difficult because most cases are asymptomatic. It is thus plausible that many asymptomatic cases in the community are undetected, thus creating a risk for an infection avalanche. This project proposes the development of a smart point-of-care ultrasound-based solution for pre-emptive screening of COVID-19.

J Serugunda, C Mwikirize, A Katumba

Development of an Efficacious Patient Management System for Uganda using Machine Learning Techniques

Patient management is a collective term to describe the series of steps involved in handling a patient at a health facility right from acquisition of laboratory samples from the patient(s) through chemo-testing and dissemination of test results to the patient (s) and /or any other stake holder involved to prescription and administration of treatment to the patient(s). This process is currently performed on manual basis that is significantly hinged on active human (health worker) participation and intelligence (tacit knowledge).

D Okello, C Mwikirize, A Katumba, W Okello, M Bomera

Real-time Tele-consultation for Cervical Cancer Screening Using a Machine Learning-enabled Mobile App.

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H Nabuuma, C Mwikirize, A Katumba, W Okello, N Kalibala, M Bomera

Smart Portable Ultrasound System for Guidance of Minimally Invasive Procedures

Non-Communicable Diseases (NCDs) such as cardiovascular disease, diabetes and cancer are a major burden in Uganda, increasing the need for requisite diagnosis/therapeutic procedures such as vascular interventions and biopsies. These procedures involve percutaneous needle insertion, and their success relies on accurate needle localization. Ultrasound imaging is the gold-standard for visualization of needle progress vis-à-vis patient anatomy. However, under ultrasound, needle localization is hindered by signal attenuation, limited field of view and artifacts. Inaccurate needle localization reduces procedure efficacy and can cause injury. In this project, these problems are addressed by developing a low-cost imaging system for diagnosis/treatment of NCDs that uses machine learning for needle localization.

C Mwikirize, A Katumba, R Byanyima, J Nabende, I Hacihaliloglu

Natural Language Processing

Neuromorphic Computing