ML Anthology

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

Sk. Imran Hossain
IJCAI 2022 pp. 5855-5856
doi:10.24963/IJCAI.2022/830 /ijcai/2022/hossain2022ijcai-early/

Abstract

Lyme disease is one of the most common infectious vector-borne diseases in the world. We extensively studied the effectiveness of convolutional neural networks for identifying Lyme dis-ease from images. Our research contribution includes dealing with lack of data, multimodal learning incorporating expert opinion elicitation, and automation of skin hair mask generation.

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Text

Hossain. "Early Diagnosis of Lyme Disease by Recognizing Erythema Migrans Skin Lesion from Images Utilizing Deep Learning Techniques." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/830

Markdown

[Hossain. "Early Diagnosis of Lyme Disease by Recognizing Erythema Migrans Skin Lesion from Images Utilizing Deep Learning Techniques." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/hossain2022ijcai-early/) doi:10.24963/IJCAI.2022/830

BibTeX

@inproceedings{hossain2022ijcai-early,
  title     = {{Early Diagnosis of Lyme Disease by Recognizing Erythema Migrans Skin Lesion from Images Utilizing Deep Learning Techniques}},
  author    = {Hossain, Sk. Imran},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {5855-5856},
  doi       = {10.24963/IJCAI.2022/830},
  url       = {https://mlanthology.org/ijcai/2022/hossain2022ijcai-early/}
}