AI-driven pathogen detection systems for rapid and accurate diagnosis
| dc.contributor.author | Deepika, M. | |
| dc.contributor.author | Murugan, Sundara Bala | |
| dc.contributor.author | Subasini, V. | |
| dc.contributor.author | Rufus, N. Herald Anantha | |
| dc.contributor.author | Atkinswestley, A. | |
| dc.contributor.author | Ali, Guma | |
| dc.date.accessioned | 2025-12-19T08:46:02Z | |
| dc.date.available | 2025-12-19T08:46:02Z | |
| dc.date.issued | 2025-09-24 | |
| dc.description | This paper presents a federated learning-based cybersecurity framework for smart healthcare systems, enabling secure, decentralized patient data analysis while preserving privacy. By integrating edge computing and advanced encryption, the approach mitigates cyber threats and enhances real-time medical decision-making. The study supports SDG 3 (Good Health and Well-being) and SDG 9 (Industry, Innovation, and Infrastructure) by promoting resilient digital health infrastructure. Although not agriculture-focused, it aligns with Uganda’s National Development Plan IV aspirations for health sector digitization and secure ICT adoption, fostering improved healthcare delivery, data protection, and technological innovation for sustainable development. | |
| dc.description.abstract | Pathogen detection at speed along with precision serves as a basis for disease diagnosis and overall control effectiveness. Several current detection methods based on culture-based techniques and PCR face drawbacks of extended processing times, high expenses, and the need for expert operators. It adopts deep learning Convolutional Neural Networks with an attention mechanism in a system designed to overcome existing limitations of pathogen detection. The model was trained using a multi-modal dataset that combined spectral and biological signals through feature extraction optimization and privacy-protected training methods based on federated learning. The proposed system reaches a 96.8% detection accuracy together with 15% reduced false alarm rates and 35% faster operation speeds than conventional models. Experimental data measurements showed that this system achieved 92.3% F1-score together with 97.1% AUC for real-time identification of pathogens. AI has proven its effectiveness through these findings in changing the way pathogen diagnostics operate. Further work will concentrate on extending datasets while creating real-time deployment protocols for clinical use. | |
| dc.identifier.citation | Deepika, M., Murugan, P. S. B., Subasini, V., Rufus, N. H. A., Atkinswestley, A., & Ali, G. (2025, May). AI-driven pathogen detection systems for rapid and accurate diagnosis. In 2025 IEEE International Conference on Computer, Electronics, Electrical Engineering & their Applications (IC2E3): 1-6. | |
| dc.identifier.uri | https://dir.muni.ac.ug/handle/20.500.12260/847 | |
| dc.language.iso | en | |
| dc.publisher | IEEE | |
| dc.subject | Deep learning | |
| dc.subject | Training | |
| dc.subject | Pathogens | |
| dc.subject | Accuracy | |
| dc.subject | Attention mechanisms | |
| dc.subject | Federated learning | |
| dc.subject | Biological system modeling | |
| dc.subject | Real-time systems | |
| dc.subject | Convolutional neural networks | |
| dc.subject | Artificial intelligence | |
| dc.title | AI-driven pathogen detection systems for rapid and accurate diagnosis | |
| dc.type | Article |