Faculty of Computing and Informatics
http://ir.must.ac.ug/xmlui/handle/123456789/68
2024-03-29T11:44:36ZComparison of Deep Learning Techniques in Detection of Sickle Cell Disease
http://ir.must.ac.ug/xmlui/handle/123456789/3461
Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease
Simon, Kawuma; Vicent, Mabirizi; Addah, Kyarisiima; Bamutura, David; Atwiine, Barnabas; Nanjebe, Deborah; Mukama, Adolf Oyesigye
Recently, transfer learning technique has proved to be powerful in enhancing development of deep learning methods for sickle cell disease (SCD) detection as a complement to the clinical method where a hemoglobin electrophoresis machine is used. This is evidenced by a number of models and algorithms with ≥90% prediction accuracy. From literature, most of the proposed methods are trained and tested on pre-trained deep learning models like VGG16, VGG19, ResNet, Inception_V3 and ReNet. However, training and testing of these methods are limited on one model and separate dataset which may lead to biased results due to implementation in variation of these models which affects results produced. To this end, there exists a need to evaluate the SCD models using the same dataset. Thus, in this research study, we carried out a comparative investigation and evaluated predominate pre-trained models used to detect SCD using the same dataset to ascertain which one has the best accuracy. We used secondary dataset obtained from an online dataset. In our study, we have discovered that Inception V3 yielded the highest accuracy of 97.3% followed by VGG19 at 97.0%, VGG16 at 91%, ResNet50 at 82% and ReNet at 67%, and the CNN-scratch model achieved 81% accuracy. Results from our study will aid researchers and industry practitioners to make decision on the best deep learning model to use while detecting SCD.
2023-01-01T00:00:00ZA Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning
http://ir.must.ac.ug/xmlui/handle/123456789/3385
A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning
Safari, Yonasi; Nakatumba-Nabende, Joyce; Nakasi, Rose; Nakibuule, Rose
Automation improves the quality of fruits through quick and accurate detection of pest and disease infections thus contributing to the country’s economic growth and productivity. Although humans can identify the fruit damage caused by pests and diseases, methods used are inconsistent, time-consuming, and variable. The surface features of fruits typically observed by consumers who seek their health benefits, affect their market value. The issue of pest and disease infections further deteriorates fruits’ quality, becoming a mounting stressor on farmers as they affect the potential income that could have been realised from production, processing and export. This article reviews various studies on detecting and classifying damages in fruits. Specifically, we review articles where state-of-the-art approaches under segmentation, image processing, machine learning, and deep learning have proved effective in developing automated systemsthataddresshurdlesassociatedwithmanualmethodsofassessingdamageusingvisualexperiences. This survey reviews 32 Journal and Conference articles spanning 13 years obtained electronically through Google Scholar, Scopus, IEEE, Science Direct, and general internet searches. This survey further presents a detailed discussion of related studies done in the past while emphasizing their strengths and limitations and presenting future research directions. It also reveals that much as the use of automated detection and classification of fruit damage has yielded promising results in the horticulture industry, more research is still needed with systems required to fully automate the detection and classification processes, especially those that are mobile phone-based towards addressing occlusion challenges.
2024-01-01T00:00:00ZA Model for Detecting the Presence of Pesticide Residues in Edible Parts of Tomatoes, Cabbages, Carrots and Green Pepper Vegetables
http://ir.must.ac.ug/xmlui/handle/123456789/3381
A Model for Detecting the Presence of Pesticide Residues in Edible Parts of Tomatoes, Cabbages, Carrots and Green Pepper Vegetables
Evarist, Nabaasa; Deborah, Natumanya; Birungi, Grace; Caroline, Nakiguli Kiwanuka; Kule, Baguma John Muhunga
With increased resistant pests and low crop yields, farmers especially in sub-saharan Africa have greatly embraced usage of chemicals. These chemicals include pesticides used in gardens for better yields and also in the stalls for longer shelf life by sellers of farm products especially fresh perishables like tomatoes, cabbages, carrots, and green paper vegetables. This, if not checked, may expose humans and animals to pesticide residues. In this research, a model for detecting the presence of pesticide residues in edible parts of vegetables (tomatoes, cabbages, carrots and green pepper) was developed. A dataset consisting of 1094 images of both contaminated and uncontaminated vegetables including tomatoes, cabbages, carrots and green pepper with a scale magnification of 800x1276 pixels taken using InfiRay P2 pro Night Vision Go Mini Infrared Thermal camera with a thermal module were taken from different daily markets in Mbarara city, South Western Uganda. Image preprocessing was done by noise removal and gray scale conversion. Both the neural network and Median filter were applied on the images. A python script was used to cluster the dataset based on chemical concentrations rates of0.1-0.8mg/kg, 0.9-1.3mg/kg and 1.4-1.7mg/kg, and this was done for both training and testing dataset. Feature extraction was done to detect the presence of mancozeb, dioxacarb and methidathion residues from the cleaned images. To test the developed model, convolutional neural networks (CNN) transfer learning models; Inception V3, VGG16, VGG19, ResNet50 and the scratch model were used. From the results obtained, Inception V3 achieved better performance compared to other transfer learning models with 96.77% followed by VGG16 at 86.98%, VGG19 at 87.56% and ResNet50 at 82.11%. Whereas the developed scratch model achieved 89.13% classification accuracy.
2022-01-01T00:00:00ZAn introduction to digital determinants of health
http://ir.must.ac.ug/xmlui/handle/123456789/3375
An introduction to digital determinants of health
Chidambaram, Swathikan; Jain, Bhav; Jain, Urvish; Mwavu, Rogers; Baru, Rama; Thomas, Beena; Greaves, Felix; Jayakumar, Shruti; Jain, Pankaj; Rojo, Marina; Battaglino, Marina Ridao; Meara, John G.; Sounderajah, Viknesh; Celi, Leo Anthony; Darzi, Ara
In recent years, technology has been increasingly incorporated within healthcare for the provision of safe and efficient delivery of services. Although this can be attributed to the benefits that can be harnessed, digital technology has the potential to exacerbate and reinforce preexisting health disparities. Previous work has highlighted how sociodemographic, economic, and political factors affect individuals’ interactions with digital health systems and are termed social determinants of health [SDOH]. But, there is a paucity of literature addressing how the intrinsic design, implementation, and use of technology interact with SDOH to influence health outcomes. Such interactions are termed digital determinants of health [DDOH]. This paper will, for the first time, propose a definition of DDOH and provide a conceptual model characterizing its influence on healthcare outcomes. Specifically, DDOH is implicit in the design of artificial intelligence systems, mobile phone applications, telemedicine, digital health literacy [DHL], and other forms of digital technology. A better appreciation of DDOH by the various stakeholders at the individual and societal levels can be channeled towards policies that are more digitally inclusive. In tandem with ongoing work to minimize the digital divide caused by existing SDOH, further work is necessary to recognize digital determinants as an important and distinct entity.
2024-01-01T00:00:00Z