{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T06:04:37Z","timestamp":1771653877834,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2023YFC3011801"],"award-info":[{"award-number":["2023YFC3011801"]}]},{"DOI":"10.13039\/501100005230","name":"the Natural Science Foundation of Chongqing","doi-asserted-by":"crossref","award":["CSTB2024NSCQ-KJFZMSX0032"],"award-info":[{"award-number":["CSTB2024NSCQ-KJFZMSX0032"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"crossref"}]},{"name":"2025Chongqing Science and Health Joint Project General Program","award":["2025MSXM034"],"award-info":[{"award-number":["2025MSXM034"]}]},{"name":"the Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJQN202400613"],"award-info":[{"award-number":["KJQN202400613"]}]},{"name":"the Chongqing Key Discipline Development Program for Medical Sciences","award":["010172"],"award-info":[{"award-number":["010172"]}]},{"name":"the 2025 State Key Laboratory of Trauma and Chemical Poisoning Open Research Project","award":["SKLO202502"],"award-info":[{"award-number":["SKLO202502"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Bacterial wound infection poses a major challenge in trauma care and can lead to severe complications such as sepsis and organ failure. Therefore, rapid and accurate identification of the pathogen, along with targeted intervention, is of vital importance for improving treatment outcomes and reducing risks. However, current detection methods are still constrained by procedural complexity and long processing times. In this study, a hyperspectral imaging (HSI) acquisition system for bacterial analysis and a multi-scale dual-domain feature fusion transformer (MDF2Former) were developed for classifying wound bacteria. MDF2Former integrates three modules: a multi-scale feature enhancement and fusion module that generates tokens with multi-scale discriminative representations, a spatial\u2013spectral dual-branch attention module that strengthens joint feature modeling, and a frequency and spatial\u2013spectral domain encoding module that captures global and local interactions among tokens through a hierarchical stacking structure, thereby enabling more efficient feature learning. Extensive experiments on our self-constructed HSI dataset of typical wound bacteria demonstrate that MDF2Former achieved outstanding performance across five metrics: Accuracy (91.94%), Precision (92.26%), Recall (91.94%), F1-score (92.01%), and Kappa coefficient (90.73%), surpassing all comparative models. These results have verified the effectiveness of combining HSI with deep learning for bacterial identification, and have highlighted its potential in assisting in the identification of bacterial species and making personalized treatment decisions for wound infections.<\/jats:p>","DOI":"10.3390\/jimaging12020090","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:57:29Z","timestamp":1771516649000},"page":"90","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MDF2Former: Multi-Scale Dual-Domain Feature Fusion Transformer for Hyperspectral Image Classification of Bacteria in Murine Wounds"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7317-1634","authenticated-orcid":false,"given":"Decheng","family":"Wu","sequence":"first","affiliation":[{"name":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wendan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xudong","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinli","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Trauma and Chemical Poisoning, Intensive Care Unit, Daping Hospital, Army Medical University, Chongqing 400042, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Trauma and Chemical Poisoning, Intensive Care Unit, Daping Hospital, Army Medical University, Chongqing 400042, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7658-993X","authenticated-orcid":false,"given":"Hao","family":"Tang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Trauma and Chemical Poisoning, Intensive Care Unit, Daping Hospital, Army Medical University, Chongqing 400042, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100561","DOI":"10.1016\/j.lansea.2025.100561","article-title":"South-East Asia regional neglected tropical disease framework: Improving control of mycetoma, chromoblastomycosis, and sporotrichosis","volume":"5","author":"Smith","year":"2025","journal-title":"Lancet Reg. Health Southeast Asia"},{"key":"ref_2","first-page":"101149","article-title":"Infectious versus chronic conditions: Time to dismantle silos in public health","volume":"48","author":"Goodman","year":"2025","journal-title":"Lancet Reg. Health Am."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1016\/S2214-109X(25)00059-2","article-title":"Top ten research priorities in global burns care: Findings from the James Lind Alliance Global Burns Research Priority Setting Partnership","volume":"13","author":"Richards","year":"2025","journal-title":"Lancet Glob. Health"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1056\/NEJMoa2104535","article-title":"Shorter treatment for nonsevere tuberculosis in African and Indian children","volume":"386","author":"Turkova","year":"2022","journal-title":"N. Engl. J. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2334","DOI":"10.1056\/NEJMoa2311981","article-title":"Corynebacterium diphtheriae Outbreak in Migrant Populations in Europe","volume":"392","author":"Hoefer","year":"2025","journal-title":"N. Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kolouchov\u00e1, I., Timkina, E., Ma\u00e1tkov\u00e1, O., Kyselov\u00e1, L., and \u0158ezanka, T. (2021). Analysis of Bacteriohopanoids from Thermophilic Bacteria by Liquid Chromatography-Mass Spectrometry. Microorganisms, 9.","DOI":"10.3390\/microorganisms9102062"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"132192","DOI":"10.1016\/j.jhazmat.2023.132192","article-title":"Graphene oxide-based colorimetric\/fluorescence dual-mode immunochromatography assay for simultaneous ultrasensitive detection of respiratory virus and bacteria in complex samples","volume":"459","author":"Cheng","year":"2023","journal-title":"J. Hazard. Mater."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5029","DOI":"10.1039\/D3LC00799E","article-title":"Label-free multidimensional bacterial characterization with an ultrawide detectable concentration range by microfluidic impedance cytometry","volume":"23","author":"Chen","year":"2023","journal-title":"Lab Chip"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105820","DOI":"10.1016\/j.ebiom.2025.105820","article-title":"Nasal biomarker testing to rule out viral respiratory infection and triage samples: A test performance study","volume":"117","author":"Amat","year":"2025","journal-title":"EBioMedicine"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104411","DOI":"10.1016\/j.biosystems.2021.104411","article-title":"A first perturbome of Pseudomonas aeruginosa: Identification of core genes related to multiple perturbations by a machine learning approach","volume":"205","author":"Mora","year":"2021","journal-title":"Biosystems"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"116005","DOI":"10.1016\/j.bios.2024.116005","article-title":"Double phage displayed peptides co-targeting-based biosensor with signal enhancement activity for colorimetric detection of Staphylococcus aureus","volume":"249","author":"Wu","year":"2024","journal-title":"Biosens. Bioelectron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/S1473-3099(25)00217-8","article-title":"Incidence of health-care-associated infections in long-term care facilities in nine European countries: A 12-month, prospective, longitudinal cohort study","volume":"25","author":"Ricchizzi","year":"2025","journal-title":"Lancet Infect. Dis."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1109\/TCSVT.2024.3459009","article-title":"Knowledge-Aware Geometric Contourlet Semantic Learning for Hyperspectral Image Classification","volume":"35","author":"Geng","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Turra, G., Conti, N., and Signoroni, A. (2015). Hyperspectral image acquisition and analysis of cultured bacteria for the discrimination of urinary tract infections. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 25\u201329 August 2015, IEEE.","DOI":"10.1109\/EMBC.2015.7318473"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103327","DOI":"10.1016\/j.infrared.2020.103327","article-title":"Comparison of variable selection algorithms on vis-NIR hyperspectral imaging spectra for quantitative monitoring and visualization of bacterial foodborne pathogens in fresh pork muscles","volume":"107","author":"Bonah","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"121838","DOI":"10.1016\/j.saa.2022.121838","article-title":"Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast","volume":"285","author":"Qiu","year":"2023","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4789","DOI":"10.1109\/JSTARS.2020.3016739","article-title":"Deep collaborative attention network for hyperspectral image classification by combining 2-D CNN and 3-D CNN","volume":"13","author":"Guo","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","first-page":"5000217","article-title":"Differential self-feedback dilated convolution network with dual-tree channel attention mechanism for hyperspectral image classification","volume":"73","author":"Xiao","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_19","first-page":"2520512","article-title":"Deep margin cosine autoencoder-based medical hyperspectral image classification for tumor diagnosis","volume":"72","author":"Wang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e202500164","DOI":"10.1002\/jbio.202500164","article-title":"Hyperspectral Imaging for Rapid Detection of Common Infected Bacteria Based on Fluorescence Effect","volume":"18","author":"Tao","year":"2025","journal-title":"J. Biophotonics"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108379","DOI":"10.1016\/j.foodcont.2021.108379","article-title":"Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms","volume":"130","author":"Kang","year":"2021","journal-title":"Food Control"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, Z., Al-Sarayreh, M., Li, Y., and Yuan, Z. (2023). Classification of tree symbiotic fungi based on hyperspectral imagery and hybrid convolutional neural networks. Front. For. Glob. Change, 6.","DOI":"10.3389\/ffgc.2023.1179910"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"19970","DOI":"10.1109\/JSEN.2025.3561018","article-title":"CBIA-Net for rapid detection of typical wound bacteria using hyperspectral imaging","volume":"25","author":"Wu","year":"2025","journal-title":"IEEE Sens. J."},{"key":"ref_24","first-page":"258619","article-title":"Deep convolutional neural networks for hyperspectral image classification","volume":"1","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral-spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4420","DOI":"10.1109\/TGRS.2018.2818945","article-title":"3-D deep learning approach for remote sensing image classification","volume":"56","author":"Hamida","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4007314","DOI":"10.1109\/TIM.2021.3117634","article-title":"Fusing multiple deep models for in vivo human brain hyperspectral image classification to identify glioblastoma tumor","volume":"70","author":"Hao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"HybridSN: Exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification","volume":"17","author":"Roy","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7570","DOI":"10.1109\/JSTARS.2021.3099118","article-title":"Hyperspectral image classification using a hybrid 3D-2D convolutional neural networks","volume":"14","author":"Ghaderizadeh","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1088\/2632-2153\/ad1d05","article-title":"ATSFCNN: A novel attention-based triple-stream fused CNN model for hyperspectral image classification","volume":"5","author":"Cai","year":"2024","journal-title":"Mach. Learn. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3566","DOI":"10.1109\/JSTARS.2021.3065987","article-title":"HResNetAM: Hierarchical residual network with attention mechanism for hyperspectral image classification","volume":"14","author":"Xue","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","first-page":"5518714","article-title":"Cross-attention spectral-spatial network for hyperspectral image classification","volume":"60","author":"Yang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5515216","DOI":"10.1109\/TGRS.2023.3286950","article-title":"A spectral-spatial fusion transformer network for hyperspectral image classification","volume":"61","author":"Liao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","first-page":"5518615","article-title":"SpectralFormer: Rethinking hyperspectral image classification with transformers","volume":"60","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","first-page":"5512319","article-title":"Multiscanning-based RNN-transormer for hyperspectral image classification","volume":"61","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.isprsjprs.2023.01.024","article-title":"From center to surrounding: An interactive learning framework for hyperspectral image classification","volume":"197","author":"Yang","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5528112","DOI":"10.1109\/TGRS.2023.3324730","article-title":"Cross-channel dynamic spatial-spectral fusion transformer for hyperspectral image classification","volume":"61","author":"Qiu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5539014","DOI":"10.1109\/TGRS.2022.3207933","article-title":"Hyperspectral image classification using group-aware hierarchical transformer","volume":"60","author":"Mei","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5535416","DOI":"10.1109\/TGRS.2022.3196771","article-title":"Lessformer: Local-enhanced spectral-spatial transformer for hyperspectral image classification","volume":"60","author":"Zou","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","first-page":"5516616","article-title":"Spatial-Spectral 1DSwin Transformer with Groupwise Feature Tokenization for Hyperspectral Image Classification","volume":"61","author":"Xu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5523815","DOI":"10.1109\/TGRS.2023.3314550","article-title":"Multiscale neighborhood attention transformer with optimized spatial pattern for hyperspectral image classification","volume":"61","author":"Qiao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","first-page":"5500813","article-title":"Deep spectral-spatial feature fusion-based multiscale adaptable attention network for hyperspectral feature extraction","volume":"72","author":"Yu","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_44","first-page":"5018013","article-title":"S2F2AN: Spatial-Spectral Fusion Frequency Attention Network for Chinese Herbal Medicines Hyperspectral Image Segmentation","volume":"74","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107709","DOI":"10.1016\/j.engappai.2023.107709","article-title":"A novel attention-enhanced network for image super-resolution","volume":"130","author":"Bo","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_46","first-page":"980","article-title":"Global filter networks for image classification","volume":"34","author":"Rao","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_47","first-page":"5501115","article-title":"Mhcformer: Multiscale hierarchical conv-aided fourierformer for hyperspectral image classification","volume":"73","author":"Shi","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","article-title":"Pvt v2: Improved baselines with pyramid vision transformer","volume":"8","author":"Wang","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_49","unstructured":"Chu, X., Tian, Z., Zhang, B., Wang, X., and Shen, C. (2021). Conditional positional encodings for vision transformers. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","article-title":"Going deeper with contextual CNN for hyperspectral image classification","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework","volume":"56","author":"Zhong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5522214","DOI":"10.1109\/TGRS.2022.3221534","article-title":"Spectral-spatial feature tokenization transformer for hyperspectral image classification","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5532117","DOI":"10.1109\/TGRS.2022.3185640","article-title":"BS2T: Bottleneck spatial-spectral transformer for hyperspectral image classification","volume":"60","author":"Song","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","first-page":"5507912","article-title":"Grouped multi-attention network for hyperspectral image spectral-spatial classification","volume":"61","author":"Lu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/2\/90\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T05:14:30Z","timestamp":1771650870000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/2\/90"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,19]]},"references-count":54,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["jimaging12020090"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12020090","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,19]]}}}