Christoph Linse, M.Sc.

Raum 1.018

Institut für Neuro- und Bioinformatik
Ratzeburger Allee 160 (Geb. 64)
23562 Lübeck

Email:
Phone:
+49 451 3101 5514
Fax:
+49 451 3101 5504

Publikationen

2021

  • Hammam Alshazly and Christoph Linse and Mohamed Abdalla and Erhardt Barth and Thomas Martinetz: COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans. PeerJ Computer Science, no. 7, pp. e655, 2021
    BibTeX Link
    @article{AlLiAbBaMa21,
    author={Hammam Alshazly and Christoph Linse and Mohamed Abdalla and Erhardt Barth and Thomas Martinetz},
    title = {{COVID-Nets}: deep {CNN} architectures for detecting {COVID-19} using chest {CT} scans},
    journal = {PeerJ Computer Science},
    volume={7},
    pages={e655},
    year={2021},
    doi = {10.7717/peerj-cs.655},
    url = {https://doi.org/10.7717/peerj-cs.655},
    }
    
    
  • Hammam Alshazly and Christoph Linse and Erhardt Barth and Thomas Martinetz: Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning. Sensors, no. 21, pp. 455, Multidisciplinary Digital Publishing Institute, 2021
    BibTeX Link
    @article{AlLiBaMa21,
    author={Hammam Alshazly and Christoph Linse and Erhardt Barth and Thomas Martinetz},
    title = {Explainable {COVID-19} {D}etection {U}sing {C}hest {CT} {S}cans and {D}eep {L}earning},
    journal = {Sensors},
    volume={21},
    number={2},
    pages={455},
    year={2021},
    publisher={Multidisciplinary Digital Publishing Institute},
    url = {https://www.mdpi.com/1424-8220/21/2/455},
    url = {http://webmail.inb.uni-luebeck.de/inb-publications/pdfs/AlLiBaMa21.pdf},
    }
    
    
  • Hammam Alshazly and Christoph Linse and Erhardt Barth and Sahar Ahmed Idris and Thomas Martinetz: Towards Explainable Ear Recognition Systems Using Deep Residual Networks. IEEE Access, pp. 1-1, 2021
    BibTeX Link
    @article{AlLiBaIdMa21,
    author = {Hammam Alshazly and Christoph Linse and Erhardt Barth and Sahar Ahmed Idris and Thomas Martinetz},
    journal = {IEEE Access},
    title = {{T}owards {E}xplainable {E}ar {R}ecognition {S}ystems {U}sing {D}eep {R}esidual {N}etworks},
    year = {2021},
    pages = {1-1},
    doi = {10.1109/ACCESS.2021.3109441},
    url = {https://doi.org/10.1109/ACCESS.2021.3109441},
    }
    
    

2020

  • Hammam Alshazly and Christoph Linse and Erhardt Barth and Thomas Martinetz: Deep Convolutional Neural Networks for Unconstrained Ear Recognition. IEEE Access, no. 8, pp. 170295-170310, IEEE, 2020
    BibTeX Link
    @article{AlLiBaMa20,
      title={Deep {C}onvolutional {N}eural {N}etworks for {U}nconstrained {E}ar {R}ecognition},
      author={Hammam Alshazly and Christoph Linse and Erhardt Barth and Thomas Martinetz},
      journal={IEEE Access},
      publisher={IEEE},
      volume={8},
      pages={170295--170310},
      year={2020},
      url = {http://webmail.inb.uni-luebeck.de/inb-publications/pdfs/AlLiBaMa20.pdf},
    }
    
    

2019

  • Hammam Alshazly and Christoph Linse and Erhardt Barth and Thomas Martinetz: Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition. sensors, no. 19, pp. 4139, Multidisciplinary Digital Publishing Institute, 2019
    BibTeX Link
    @article{AlLiBaMa19,
    author={Hammam Alshazly and Christoph Linse and Erhardt Barth and Thomas Martinetz},
    title = {Ensembles of {D}eep {L}earning {M}odels and {T}ransfer {L}earning for {E}ar {R}ecognition},
    journal = {sensors},
    volume={19},
    number={19},
    pages={4139},
    year={2019},
    publisher={Multidisciplinary Digital Publishing Institute},
    url = {https://www.mdpi.com/1424-8220/19/19/4139},
    url = {http://webmail.inb.uni-luebeck.de/inb-publications/pdfs/AlLiBaMa19.pdf},
    }
    
    
  • Hammam Alshazly and Christoph Linse and Erhardt Barth and Thomas Martinetz: Handcrafted versus CNN Features for Ear Recognition. Symmetry, no. 11, pp. 1493, 2019
    BibTeX Link
    @article{AlLiBaMa19a,
      title={{Handcrafted versus CNN Features for Ear Recognition}},
      author={Hammam Alshazly and Christoph Linse and Erhardt Barth and Thomas Martinetz},
      journal={Symmetry},
      volume={11},
      number={12},
      pages={1493},
      year={2019},
      url = {http://webmail.inb.uni-luebeck.de/inb-publications/pdfs/AlLiBaMa19a.pdf},
    }