A hybrid dense convolutional network and fuzzy inference system for pneumonia diagnosis with dynamic symptom tracking
This hybrid model offers a robust, interpretable, and clinically relevant decision-support tool for pneumonia diagnosis. It bridges high-performance AI with real-world medical decision-making, especially in settings with limited radiological expertise.
Authors
- Sovit Jung Baral
- João C. Ferreira (ORCID: https://orcid.org/0000-0002-6662-0806)
- Rabindra Bista (ORCID: https://orcid.org/0000-0002-0638-5840)
- Sanjog Sigdel (ORCID: https://orcid.org/0000-0002-0470-860X)
Institutions
- Molde University College (NO)
- Instituto de Novas Tecnologias (PT)
- Institute for Systems Engineering and Computers (PT)
- Kathmandu University (NP)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-10-22
- DOI
- https://doi.org/10.1371/journal.pone.0334899
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00