A multi-attention mechanism-improved ResNet18 approach for lithology identification and classification.

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Title: A multi-attention mechanism-improved ResNet18 approach for lithology identification and classification.
Authors: Zhang Y; Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering, Guilin University of Technology, Guilin, China.; Guangxi Key Laboratory of Green Building Materials and Construction Industrialization, Guilin University of Technology, Guilin, China., Zhang F; Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering, Guilin University of Technology, Guilin, China.; Guangxi Key Laboratory of Green Building Materials and Construction Industrialization, Guilin University of Technology, Guilin, China., Li J; Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering, Guilin University of Technology, Guilin, China.; Guangxi Key Laboratory of Green Building Materials and Construction Industrialization, Guilin University of Technology, Guilin, China., Huang L; Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering, Guilin University of Technology, Guilin, China.; Guangxi Key Laboratory of Green Building Materials and Construction Industrialization, Guilin University of Technology, Guilin, China., Li R; Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering, Guilin University of Technology, Guilin, China.; Guangxi Key Laboratory of Green Building Materials and Construction Industrialization, Guilin University of Technology, Guilin, China.
Source: PloS one [PLoS One] 2026 Oct 27; Vol. 21 (10), pp. e0361625. Date of Electronic Publication: 2026 Oct 27 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Database: MEDLINE Ultimate
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ISSN:1932-6203
DOI:10.1371/journal.pone.0361625