Fetal health classification: a deep learning model with enhanced interpretability and lightweight deployment.
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| Title: | Fetal health classification: a deep learning model with enhanced interpretability and lightweight deployment. |
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| Authors: | Hasan R; Department of Science and Engineering, Southampton Solent University, Southampton, SO14 0YN, UK. raza.hasan@solent.ac.uk., Dattana V; Department of Computer Science and Management Information System, College of Management & Technology, P.O. Box 680, Barka, 320, Oman., Mahmood S; Department of Computer Science, Nazeer Hussain University, ST-2, Near Karimabad, Karachi, 75950, Sindh, Pakistan., Abbas A; Department of Computing and Electronics Engineering, Middle East College, Muscat, Oman., Sojitra KK; School of Technology and Maritime Industries, Southampton Solent University, E Park Terrace, Southampton, Hampshire, SO14 0YN, UK., Hussain S; Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, NE1 8QH, UK. |
| Source: | Medical & biological engineering & computing [Med Biol Eng Comput] 2026 Jun; Vol. 64 (6), pp. 2057-2083. Date of Electronic Publication: 2026 Apr 06. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1741-0444 (Electronic) Linking ISSN: 01400118 NLM ISO Abbreviation: Med Biol Eng Comput Subsets: MEDLINE; In Process |
| Database: | MEDLINE Ultimate |
| ISSN: | 1741-0444 |
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| DOI: | 10.1007/s11517-026-03525-z |
