Hammam Alshazly, PhD
Raum 1.018
Institut für Neuro- und Bioinformatik
Ratzeburger Allee 160 (Geb. 64)
23562 Lübeck
Email: | alshazly(at)inb.uni-luebeck.de |
Phone: | +49 451 3101 5513 |
Fax: | +49 451 3101 5504 |
Publikationen
2021
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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
@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}, }
2020
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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
@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
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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
@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}, }
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Hammam Alshazly and Christoph Linse and Erhardt Barth and Thomas Martinetz: Handcrafted versus CNN Features for Ear Recognition. Symmetry, no. 11, pp. 1493, 2019
@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}, }