We’re excited to launch the first AFRICAI Journal Club – in the series where we come together to critically engage with cutting-edge scientific papers in the field of AI and healthcare. We are pleased to welcome Ayomide Benjamen Oladele.
Ayomide Benjamen Oladele is a Software Engineer at Medical Artificial Intelligence, Laboratory, MAI Lab in Lagos, Nigeria. I develop AI-powered solutions for healthcare. Passionate about the intersection of artificial intelligence and medicine, my goal is to advance drug discovery and improve medical diagnostics through innovative technology. Outside of work, I enjoy football, music, political conversations, and sharing the gospel.
Why this paper?
This paper tackles a critical challenge in low-resource healthcare: the lack of advanced tools for early and accurate brain tumor diagnosis. By introducing an efficient segmentation method that minimizes computing demands, it offers a practical solution for regions with limited infrastructure. Sharing this work with the AFRICAI community can spark vital discussions on accessible AI and inspire innovations that promote equitable healthcare worldwide.
Event Details
• Date: Tuesday, April 29, 2025
• Time: 14:00-15.00 WAT (West Africa Time), 15.00-16.00 CAT (Central Africa Time), 15:00-16:00 CET (Central European Summer Time)
• Duration: 60 minutes (30-minute talk + 30-minute discussion)
• Location: Online via Zoom (link shared upon registration)
Full article Reference
Mizanu et al., (2024). Optimized Brain Tumor Segmentation for Resource Constrained Settings: VGG-Infused U-Net Approach. In A. Al-Badarneh et al. (Eds.), Deep Learning Techniques for Biomedical and Health Informatics (pp. 15–32). Springer.
View Recording of Journal Club on Youtube:
