Real-time Tele- consultation for Cervical Cancer Screening Using a Machine Learning-enabled Mobile App
Cervical cancer, in particular, is the fourth most common cancer in women and the seventh overall, responsible for 569,847 deaths globally (World Health Organization 2018). Over 85% of the global cervical cancer burden occurs in LMICs. In Uganda, cervical cancer is the most common cancer in women, and the incidence is increasing. Effective identification and treatment of precancerous cervical lesions or early-stage cervical cancer is the most critical intervention in the fight against cervical cancer. Women are screened by visual inspection with acetic acid (VIA), and those with lesions are treated before the lesions become cancerous. HIV-positive women should be screened annually, while those who are negative should be screened every 3 years (Nakisige, Schwartz, and Ndira 2017). Lack of awareness among the healthcare workers at frontline facilities, who lack oncology knowledge, is another challenge. Another challenge is the lack of means to notify patients when they are due for (re)screening. To promote early detection in rural areas, the Uganda Cancer Institute (UCI) set up a mobile colposcopy at its satellite clinic in Mayuge, where health workers use it to capture cervical images and transmit them to UCI for experienced gynaecologists to examine them and advise the health workers on more complex cases. To make this consultation real-time, we developed a mobile app through which the health workers upload the images that the gynaecologists can view immediately and enter their impressions, which the health workers can also view immediately. The mobile app is accessible on any Android smartphone. The app has machine learning (ML) capabilities for classifying the images taken by the mobile colposcope. The images are classified as classified as “positive”, “negative” and also indicate where lesions are. The ML classifications, coupled with VIA screening, help to improve the accuracy of the screening process. If gynaecologists are unavailable, the ML capability can assist the health worker in their decision. Finally, if the patient needs re-screening, the app will be able to send them a text message reminder for their appointment.
Development of ML Datasets for Crop Pest and Disease Diagnosis based on Crop Imagery and Spectrometry Data
The current state of data collection and crop pest and disease diagnosis is transitioning from disease identification using visible symptoms to the use of data-driven solutions applying machine learning and computer vision techniques. Smallholder farmers and agricultural experts are
equipped with mobile phones loaded with software to collect field-level Geo-coded automatically and time-stamped data. We have demonstrated the potential for the use of these tools for disease diagnosis for beans, cassava, bananas, and tomatoes. However, the image data previously collected has not been sufficiently curated, prepared, and shared with the wider machine-learning community. Moreover, by the time image data is captured, diseases have already manifested in different parts of the plant, and little can be done to salvage the situation. Although recent studies have shown that the presence of diseases can be detected from leaf spectral six weeks before the appearance of visual symptoms, this work has been limited. It is essential to transfer this work from the controlled screen house environment to the field for reproducibility and investigate how the plant metabolite is affected under these Conditions.
Design of a Machine Learning-Based Traffic Control System
The project investigates the traffic jam problem at one of the junctions in Kampala, Uganda, Wandegeya junction. At Wandegeya junction, existing traffic light control causes long delays, air pollution, energy waste, accidents, and many other problems. Through Kampala Capital City Authority (KCCA), the government of Uganda has tried to solve this problem using different technologies like radar, but it did not help much. The project studies the traffic signal’s duration based on the data collected manually by counting cars and data from KCCA. The machine learning-based model was developed to control the traffic light(agent). Q-learning was used; it is a model-free reinforcement algorithm. Q-learning learned the actions of the agent and powers neural network to predict better steps to take. The model was evaluated via Simulation of Urban Mobility (SUMO) in a vehicular network, and the simulation results showed the efficiency of this model in controlling traffic lights.
Design of a Machine Learning Pothole Detection System
With an increasing number of potholes on the roads that take a while before they are repaired, their adverse effects like accidents, traffic jam, flooding during the rainy season and damage to motor vehicles keep affecting road users. However, automating the process of the location and detection of these potholes can cut down the amount of time it takes for these potholes to be repaired from when they are first located. This report therefore presents the Design of a Deep Learning Based Pothole Detection System which involves a deep analysis on the use of machine learning and Convolutional Neural Networks in computer vision and object detection. This study will involve training a machine learning model with pothole dataset and later developing a model that can detect the presence of these potholes on the roads. Data collection was done by taking pictures in the form of videos of different roads inside a moving vehicle by use of a mobile phone placed on a car windscreen. A total of 836 images were collected, the images were resized and labelled by drawing bounding boxes around the potholes in the images. The images were then converted to a format compatible with the training algorithm, the COCO dataset format. For training the EfficientDet algorithm was used which is a high accuracy, high efficiency algorithm with each layer images going through different kinds of optimization till the box layer. Model training using the efficientdet algorithm was successful and a machine learning model that detects potholes with an Average Precision of 0.52515274 was developed. A pothole dataset of 836 images were collected and is stored in a database.
Digi Eye Sweet Potato Project: Predicting Sweet Potato Sensory Attributes using Image Analysis
During the sweet potato breeding process, phenotypes are measured for different quality traits, which differ from one crop to another. Their genetic, nutritional, organoleptic, biological, and morphological characteristics are classified into other groups. Color and appearance are morphological traits typically used by consumers as visual cues to decide underlying quality attributes such as taste, cooking quality, and texture of a particular crop or product. This means that color and appearance are crucial indicators for targeting end-user preferences for breeding nutritious food crops in Africa. The assessment of the organoleptic attributes is especially important for the breeders, including characteristics like taste, texture, and mealiness. These attributes are scored by a human-trained panel that tastes different samples of sweet potatoes and also scores some of the morphological traits like color. The assessment of this panel is used to inform the selection of samples that are progressed during the breeding and release of new varieties. In order to achieve a high-throughput sensory evaluation chain, image-based analysis is used to evaluate different quality traits of the sweet potato samples. Images were taken using the DigiEye machine, a non-contact digital imaging system. The color and appearance of raw and cooked products can be instantly and accurately captured and analyzed under a stable light environment. After capturing the images, the attributes are analyzed using computer vision machine learning techniques, and advanced tools that will increase the throughput of the human sensory panel.
Deep Learning-assisted Screening of COVID-19 using Point-of-Care Ultrasound in Uganda
The COVID-19 pandemic has underscored the need for rapid, reliable diagnostic tools, especially in resource-limited settings like Uganda. This project investigates the use of deep learning algorithms for screening COVID-19 via point-of-care ultrasound (POCUS), a portable and cost-effective imaging technique. In Uganda, traditional diagnostic methods such as RT-PCR and chest CT scans are often costly and less accessible, particularly in rural areas. POCUS presents a viable alternative for detecting lung abnormalities associated with COVID-19. The aim of this study is to develop a deep learning-assisted system that automates the interpretation of lung ultrasound images, focusing on identifying COVID-19-specific patterns. A dataset of annotated lung ultrasound images from both COVID-19 patients and healthy individuals will be used to train convolutional neural networks (CNNs). The performance of the model will be validated against expert clinical interpretations to ensure high accuracy and reliability. Additionally, the project will explore the feasibility of deploying this system in real-time on portable POCUS devices used in Uganda, enabling rapid COVID-19 screenings in remote and underserved regions. By providing an affordable, scalable, and effective diagnostic tool, this approach has the potential to significantly improve COVID-19 detection in Uganda, reducing the burden on healthcare infrastructure. Furthermore, the success of this initiative could catalyze the integration of artificial intelligence into broader healthcare solutions in resource-constrained settings.
Cervical cancer, in particular, is the fourth most common cancer in women and the seventh overall, responsible for 569,847 deaths globally (World Health Organization 2018). Over 85% of the global cervical cancer burden occurs in LMICs. In Uganda, cervical cancer is the most common cancer in women, and the incidence is increasing. Effective identification and treatment of precancerous cervical lesions or early-stage cervical cancer is the most critical intervention in the fight against cervical cancer. Women are screened by visual inspection with acetic acid (VIA), and those with lesions are treated before the lesions become cancerous. HIV-positive women should be screened annually, while those who are negative should be screened every 3 years (Nakisige, Schwartz, and Ndira 2017). Lack of awareness among the healthcare workers at frontline facilities, who lack oncology knowledge, is another challenge. Another challenge is the lack of means to notify patients when they are due for (re)screening. To promote early detection in rural areas, the Uganda Cancer Institute (UCI) set up a mobile colposcopy at its satellite clinic in Mayuge, where health workers use it to capture cervical images and transmit them to UCI for experienced gynaecologists to examine them and advise the health workers on more complex cases. To make this consultation real-time, we developed a mobile app through which the health workers upload the images that the gynaecologists can view immediately and enter their impressions, which the health workers can also view immediately. The mobile app is accessible on any Android smartphone. The app has machine learning (ML) capabilities for classifying the images taken by the mobile colposcope. The images are classified as classified as “positive”, “negative” and also indicate where lesions are. The ML classifications, coupled with VIA screening, help to improve the accuracy of the screening process. If gynaecologists are unavailable, the ML capability can assist the health worker in their decision. Finally, if the patient needs re-screening, the app will be able to send them a text message reminder for their appointment.
The current state of data collection and crop pest and disease diagnosis is transitioning from disease identification using visible symptoms to the use of data-driven solutions applying machine learning and computer vision techniques. Smallholder farmers and agricultural experts are
equipped with mobile phones loaded with software to collect field-level Geo-coded automatically and time-stamped data. We have demonstrated the potential for the use of these tools for disease diagnosis for beans, cassava, bananas, and tomatoes. However, the image data previously collected has not been sufficiently curated, prepared, and shared with the wider machine-learning community. Moreover, by the time image data is captured, diseases have already manifested in different parts of the plant, and little can be done to salvage the situation. Although recent studies have shown that the presence of diseases can be detected from leaf spectral six weeks before the appearance of visual symptoms, this work has been limited. It is essential to transfer this work from the controlled screen house environment to the field for reproducibility and investigate how the plant metabolite is affected under these Conditions.
The project investigates the traffic jam problem at one of the junctions in Kampala, Uganda, Wandegeya junction. At Wandegeya junction, existing traffic light control causes long delays, air pollution, energy waste, accidents, and many other problems. Through Kampala Capital City Authority (KCCA), the government of Uganda has tried to solve this problem using different technologies like radar, but it did not help much. The project studies the traffic signal’s duration based on the data collected manually by counting cars and data from KCCA. The machine learning-based model was developed to control the traffic light(agent). Q-learning was used; it is a model-free reinforcement algorithm. Q-learning learned the actions of the agent and powers neural network to predict better steps to take. The model was evaluated via Simulation of Urban Mobility (SUMO) in a vehicular network, and the simulation results showed the efficiency of this model in controlling traffic lights.
With an increasing number of potholes on the roads that take a while before they are repaired, their adverse effects like accidents, traffic jam, flooding during the rainy season and damage to motor vehicles keep affecting road users. However, automating the process of the location and detection of these potholes can cut down the amount of time it takes for these potholes to be repaired from when they are first located. This report therefore presents the Design of a Deep Learning Based Pothole Detection System which involves a deep analysis on the use of machine learning and Convolutional Neural Networks in computer vision and object detection. This study will involve training a machine learning model with pothole dataset and later developing a model that can detect the presence of these potholes on the roads. Data collection was done by taking pictures in the form of videos of different roads inside a moving vehicle by use of a mobile phone placed on a car windscreen. A total of 836 images were collected, the images were resized and labelled by drawing bounding boxes around the potholes in the images. The images were then converted to a format compatible with the training algorithm, the COCO dataset format. For training the EfficientDet algorithm was used which is a high accuracy, high efficiency algorithm with each layer images going through different kinds of optimization till the box layer. Model training using the efficientdet algorithm was successful and a machine learning model that detects potholes with an Average Precision of 0.52515274 was developed. A pothole dataset of 836 images were collected and is stored in a database.
During the sweet potato breeding process, phenotypes are measured for different quality traits, which differ from one crop to another. Their genetic, nutritional, organoleptic, biological, and morphological characteristics are classified into other groups. Color and appearance are morphological traits typically used by consumers as visual cues to decide underlying quality attributes such as taste, cooking quality, and texture of a particular crop or product. This means that color and appearance are crucial indicators for targeting end-user preferences for breeding nutritious food crops in Africa. The assessment of the organoleptic attributes is especially important for the breeders, including characteristics like taste, texture, and mealiness. These attributes are scored by a human-trained panel that tastes different samples of sweet potatoes and also scores some of the morphological traits like color. The assessment of this panel is used to inform the selection of samples that are progressed during the breeding and release of new varieties. In order to achieve a high-throughput sensory evaluation chain, image-based analysis is used to evaluate different quality traits of the sweet potato samples. Images were taken using the DigiEye machine, a non-contact digital imaging system. The color and appearance of raw and cooked products can be instantly and accurately captured and analyzed under a stable light environment. After capturing the images, the attributes are analyzed using computer vision machine learning techniques, and advanced tools that will increase the throughput of the human sensory panel.
The COVID-19 pandemic has underscored the need for rapid, reliable diagnostic tools, especially in resource-limited settings like Uganda. This project investigates the use of deep learning algorithms for screening COVID-19 via point-of-care ultrasound (POCUS), a portable and cost-effective imaging technique. In Uganda, traditional diagnostic methods such as RT-PCR and chest CT scans are often costly and less accessible, particularly in rural areas. POCUS presents a viable alternative for detecting lung abnormalities associated with COVID-19. The aim of this study is to develop a deep learning-assisted system that automates the interpretation of lung ultrasound images, focusing on identifying COVID-19-specific patterns. A dataset of annotated lung ultrasound images from both COVID-19 patients and healthy individuals will be used to train convolutional neural networks (CNNs). The performance of the model will be validated against expert clinical interpretations to ensure high accuracy and reliability. Additionally, the project will explore the feasibility of deploying this system in real-time on portable POCUS devices used in Uganda, enabling rapid COVID-19 screenings in remote and underserved regions. By providing an affordable, scalable, and effective diagnostic tool, this approach has the potential to significantly improve COVID-19 detection in Uganda, reducing the burden on healthcare infrastructure. Furthermore, the success of this initiative could catalyze the integration of artificial intelligence into broader healthcare solutions in resource-constrained settings.