% alzhub.org library filter
% Source: https://alzhub.org/research?score_min=67&atag=deep
% 4 entries, in the order the page lists them. Built from alzhub.org's own records.
% 0 entries have no readable author in the source record and carry no author field.

@article{jo2019-31481890,
  author  = {Jo, T. and Nho, K. and Saykin, A. J.},
  title   = {{Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data}},
  journal = {Frontiers in aging neuroscience},
  year    = {2019},
  doi     = {10.3389/fnagi.2019.00220},
  pmid    = {31481890},
  url     = {https://doi.org/10.3389/fnagi.2019.00220}
}

@article{vimbi2024-38578524,
  author  = {Vimbi, V. and Shaffi, N. and Mahmud, M.},
  title   = {{Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection}},
  journal = {Brain informatics},
  year    = {2024},
  doi     = {10.1186/s40708-024-00222-1},
  pmid    = {38578524},
  url     = {https://doi.org/10.1186/s40708-024-00222-1}
}

@article{chang2021-33803217,
  author  = {Chang, C. H. and Lin, C. H. and Lane, H. Y.},
  title   = {{Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease}},
  journal = {International journal of molecular sciences},
  year    = {2021},
  doi     = {10.3390/ijms22052761},
  pmid    = {33803217},
  url     = {https://doi.org/10.3390/ijms22052761}
}

@article{alarjani2024-39650470,
  author  = {Alarjani, Maitha and Almarri, Badar},
  title   = {{fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review}},
  journal = {PeerJ. Computer science},
  year    = {2024},
  doi     = {10.7717/peerj-cs.2302},
  pmid    = {39650470},
  url     = {https://doi.org/10.7717/peerj-cs.2302}
}