Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images

@article{Hossain2022ExploringCN,
  title={Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images},
  author={Sk. Imran Hossain and Jocelyn de Go{\"e}r de Herve and Md Shahriar Hassan and Delphine Martineau and Evelina Petrosyan and Violaine Corbin and Jean Beytout and Isabelle Lebert and Jonas Durand and Irene Carravieri and Annick Brun-Jacob and Pascale Frey-Klett and Elisabeth Baux and C{\'e}line Cazorla and Carole Eldin and Yves Hansmann and Sol{\`e}ne Patrat-Delon and Thierry Prazuck and Alice Raffetin and Pierre Tattevin and Gwena{\"e}l Vourc'h and Olivier Lesens and Engelbert Mephu Nguifo},
  journal={Computer methods and programs in biomedicine},
  year={2022},
  volume={215},
  pages={
          106624
        }
}
4 Citations

Expert Opinion Elicitation for Assisting Deep Learning based Lyme Disease Classifier with Patient Data

TLDR
The elicited probability scores can be utilized to make image based deep learning Lyme disease pre-scanners robust and assist the deep learning model with a probability score calculated from patient data.

Early Diagnosis of Lyme Disease by Recognizing Erythema Migrans Skin Lesion from Images Utilizing Deep Learning Techniques

TLDR
This work extensively studied the effectiveness of convolutional neural networks for identifying Lyme dis-ease from images to deal with lack of data, multimodal learning incorporating expert opinion elicitation, and automation of skin hair mask generation.

Classification of Skin Lesion through Active Learning Strategies.

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