Towards Sustainable Oceans: Deep Learning Models for Accurate COTS Detection in Underwater Images

Object detection is one of the main tasks in computer vision, which includes image classification and localization. The application of object detection is now widespread as it powers various applications such as self-driving cars, robotics, biometrics, surveillance, satellite image analysis, and in healthcare, to mention just a few. Deep learning has taken computer vision to a different horizon. One of the areas that will benefit immensely from deep learning computer vision is the detection of killer starfish, the crown-of-thorns starfish (COTS). For decades, this killer starfish has dealt a big blow to the Great Barrier Reef in Australia, the world’s largest system of reefs, and in other places too. In addition to impacting negatively environmentally, it affects revenue generation from reef tourism. Hence, reef managers and authorities want to control the populations of crown-of-thorns starfish, which have been observed to be the culprits. The deep learning technique offers real-time and robust detection of this creature more than earlier traditional methods that were used to detect these creatures. This thesis work is part of a competition for a deep learning approach to detect COTS in real time by building an object detector trained using underwater images. This offers a solution to control the outbreaks in the population of these animals. Deep learning methods of Artificial Intelligence (AI) have gained popularity today because of its speed and high accuracy in detection and have performed better than the earlier traditional methods. They can be used in real-time object detection, and they owe their speed to convolutional neural networks (CNN). The thesis gives a comprehensive literature review of the journey so far in the field of computer vision and how deep learning methods can be applied to detect COTS. It also outlines the steps involved in the implementation of the model using the state-of-the-art computer vision algorithm known for its speed and accuracy – YOLOv8. The COTS detection model was trained using the custom dataset provided by the organizers of the competition, harnessing the powers of deep learning methods such as transfer learning, data augmentation, and preprocessing of underwater images to achieve high accuracy. Evaluation of the results obtained from the training showed a mean average precision of 0.803mAP at IoU of 0.5-0.95, acknowledging the detector model’s versatility in making accurate detection at different confidence levels. This supports the hypothesis that when we use pre trained model, this enhances the performance of our model for better object detection tasks. Certainly, better detection accuracy is one way to detect killer starfish, the crown-of-thorns starfish (COTS), and help protect the oceans

Effect of educational intervention programme on the health-related quality of life (HRQOL) of individuals with type 2 diabetes mellitus in South-East, Nigeria

Abstract Background Diabetes is one of the most important chronic diseases that have a great impact on health as people with diabetes are constantly being reminded of their disease daily; they have to eat carefully, exercise, and test their blood glucose. They often feel challenged by their disease because of its day-to-day management demands and these affect their quality of life. The study aimed at determining the effect of an educational intervention program on the quality of life of Individuals with type 2 Diabetes Mellitus in South East, Nigeria. Methods A quasi-experimental controlled study involving three hundred and eighty-two (382) type 2 DM persons recruited from the tertiary health institutions in South East, Nigeria, and randomly assigned to intervention and control groups respectively. Data was collected from the diabetic clinics of the health institutions using the SF – 36 questionnaires. Pretest data collection was done, and thereafter, education on self-care was given to the intervention group. After a 6months follow-up, post-test data were collected from both groups. Analysis was done using an Independent t-test, Analysis of Covariance (ANCOVA), Paired Samples Test, and Spearman rank order correlation at 0.05 alpha level. Results The control group indicated significantly higher mean HRQOL scores in most domains of the HRQOL before intervention (t = -1.927 to -6.072, p < 0.05). However, 6 months after the intervention, the mean HRQOL scores of the intervention group increased significantly in all the domains of HRQOL (p < 0.05) with an effect size of 0.14 (Eta squared). A comparison of the two groups shows a statistically significant difference (64.72 ± 10.96 vs. 58.85 ± 15.23; t = 4.349. p = 0.001) after the intervention. Age was inversely correlated with some domains of HRQOL; as age increases, HRQOL decreases in those domains. Gender had no significant influence on HRQOL. Conclusion Educational intervention was effective in improving HRQOL in individuals with type 2 DM. Hence, it is recommended for inclusion in all diabetes care plans

Feasibility of Malaria Diagnosis and Management in Burkina Faso, Nigeria, and Uganda:A Community-Based Observational Study

Background. Malaria-endemic countries are encouraged to increase, expedite, and standardize care based on parasite diagnosis and treat confirmed malaria using oral artemisinin-based combination therapy (ACT) or rectal artesunate plus referral when patients are unable to take oral medication. Methods. In 172 villages in 3 African countries, trained community health workers (CHWs) assessed and diagnosed children aged between 6 months and 6 years using rapid histidine-rich protein 2 (HRP2)–based diagnostic tests (RDTs). Patients coming for care who could take oral medication were treated with ACTs, and those who could not were treated with rectal artesunate and referred to hospital. The full combined intervention package lasted 12 months. Changes in access and speed of care and clinical course were determined through 1746 random household interviews before and 3199 during the intervention. Results. A total of 15 932 children were assessed: 6394 in Burkina Faso, 2148 in Nigeria, and 7390 in Uganda. Most children assessed (97.3% [15 495/15 932]) were febrile and most febrile cases (82.1% [12 725/15 495]) tested were RDT positive. Almost half of afebrile episodes (47.6% [204/429]) were RDT positive. Children eligible for rectal artesunate contributed 1.1% of episodes. The odds of using CHWs as the first point of care doubled (odds ratio [OR], 2.15; 95% confidence interval [CI], 1.9–2.4; P < .0001). RDT use changed from 3.2% to 72.9% (OR, 80.8; 95% CI, 51.2–127.3; P < .0001). The mean duration of uncomplicated episodes reduced from 3.69 ± 2.06 days to 3.47 ± 1.61 days, Degrees of freedom (df) = 2960, Student's t (t) = 3.2 (P = .0014), and mean duration of severe episodes reduced from 4.24 ± 2.26 days to 3.7 ± 1.57 days, df = 749, t = 3.8, P = .0001. There was a reduction in children with danger signs from 24.7% before to 18.1% during the intervention (OR, 0.68; 95% CI, .59–.78; P < .0001). Conclusions. Provision of diagnosis and treatment via trained CHWs increases access to diagnosis and treatment, shortens clinical episode duration, and reduces the number of severe cases. This approach, recommended by the World Health Organization, improves malaria case management. Clinical Trials Registration. ISRCTN13858170

Impact of Improving Community-Based Access to Malaria Diagnosis and Treatment on Household Costs

Background. Community health workers (CHWs) were trained in Burkina Faso, Nigeria, and Uganda to diagnose febrile children using malaria rapid diagnostic tests, and treat positive malaria cases with artemisinin-based combination therapy (ACT) and those who could not take oral medicines with rectal artesunate. We quantified the impact of this intervention on private household costs for childhood febrile illness. Methods. Households with recent febrile illness in a young child in previous 2 weeks were selected randomly before and during the intervention and data obtained on household costs for the illness episode. Household costs included consultation fees, registration costs, user fees, diagnosis, bed, drugs, food, and transport costs. Private household costs per episode before and during the intervention were compared. The intervention's impact on household costs per episode was calculated and projected to districtwide impacts on household costs. Results. Use of CHWs increased from 35% of illness episodes before the intervention to 50% during the intervention (P < .0001), and total household costs per episode decreased significantly in each country: from US Dollars (USD) $4.36 to USD$1.54 in Burkina Faso, from USD $3.90 to USD$2.04 in Nigeria, and from USD $4.46 to USD$1.42 in Uganda (all P < .0001). There was no difference in the time used by the child's caregiver to care for a sick child (59% before intervention vs 51% during intervention spent ≤2 days). Using the most recent population figures for each study district, we estimate that the intervention could save households a total of USD $29 965, USD$254 268, and USD $303 467, respectively, in the study districts in Burkina Faso, Nigeria, and Uganda. Conclusions. Improving access to malaria diagnostics and treatments in malaria-endemic areas substantially reduces private household costs. The key challenge is to develop and strengthen community human resources to deliver the intervention, and ensure adequate supplies of commodities and supervision. We demonstrate feasibility and benefit to populations living in difficult circumstances. Clinical Trials Registration. ISRCTN13858170