{"type":"ScholarlyArticle","title":"Early Diagnosis of Lyme Disease by Recognizing Erythema Migrans Skin Lesion from Images Utilizing Deep Learning Techniques","authors":[{"name":"Sk Imran Hossain","slug":"sk-imran-hossain","given":"Sk Imran","family":"Hossain","affiliation":"Université Clermont Auvergne , CNRS , ENSMSE , LIMOS , F-63000 Clermont-Ferrand , France"}],"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.","doi":"10.24963/ijcai.2022/830","url":"https://ijcai.org/proceedings/2022/830","pdf":"https://ijcai.org/proceedings/2022/0830.pdf","markdown":"https://ijcai.org/proceedings/2022/830.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/830","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":830,"pages":{"first":5855,"last":5856},"keywords":["Computer Vision (CV): General","Machine Learning (ML): General"],"videos":[],"references":[{"position":1,"raw":"[Burlina et al., 2020] Philippe Burlina, Neil Joshi, Philip Mathew, William Paul, Alison Rebman, and John Aucott. AI-based detection of erythema migrans and disambigua- tion against other skin lesions. Computers in Biology and Medicine, 125:103977, oct 2020.","title":"AI-based detection of erythema migrans and disambiguation against other skin lesions","authors":"Philippe Burlina, Neil Joshi, Philip Mathew, William Paul, Alison Rebman, John Aucott","year":"2020","venue":"Computers in Biology and Medicine","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Čuk et al., 2014] Erik Čuk, Matjaž Gams, Matej Možek, Franc Strle, Vera Maraspin Čarman, and Jurij Tasič. Su- pervised visual system for recognition of erythema mi- grans, an early skin manifestation of lyme borreliosis. Strojniski Vestnik -Journal of Mechanical Engineering, 60(2):115-123, feb 2014.","title":"Erik Čuk, Matjaž Gams, Matej Možek, Franc Strle, Vera Maraspin Čarman, and Jurij Tasič. Supervised visual system for recognition of erythema migrans, an early skin manifestation of lyme borreliosis","authors":"Čuk","year":"2014","venue":"Strojniski Vestnik -Journal of Mechanical Engineering","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Hossain et al., 2022] Sk Imran Hossain, Jocelyn de Goër de Herve, Md Shahriar Hassan, Delphine Martineau, Evelina Petrosyan, Violaine Corbin, Jean Beytout, Isabelle Lebert, Jonas Durand, Irene Carravieri, Annick Brun-Jacob, Pas- cale Frey-Klett, Elisabeth Baux, Céline Cazorla, Car- ole Eldin, Yves Hansmann, Solene Patrat-Delon, Thierry Prazuck, Alice Raffetin, Pierre Tattevin, Gwenaël Vourc'h, Olivier Lesens, and Engelbert Mephu Nguifo. Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images. Com- puter Methods and Programs in Biomedicine, 215:106624, mar 2022.","title":"Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images","authors":"Hossain","year":"2022","venue":"Computer Methods and Programs in Biomedicine","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Tschandl et al., 2018] Philipp Tschandl, Cliff Rosendahl, and Harald Kittler. The HAM10000 dataset, a large col- lection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data, 5(1):1-9, aug 2018.","title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","authors":"Philipp Tschandl, Cliff Rosendahl, Harald Kittler","year":"2018","venue":"Scientific Data","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}