H
Hitoshi Iyatomi
Researcher at Hosei University
Publications - 129
Citations - 4054
Hitoshi Iyatomi is an academic researcher from Hosei University. The author has contributed to research in topics: Computer science & Plant disease. The author has an hindex of 24, co-authored 118 publications receiving 3346 citations. Previous affiliations of Hitoshi Iyatomi include Keio University & Tokai University.
Papers
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Journal ArticleDOI
A Methodological Approach to the Classification of Dermoscopy Images
M. Emre Celebi,Hassan A. Kingravi,Bakhtiyar Uddin,Hitoshi Iyatomi,Y. Alp Aslandogan,William V. Stoecker,Randy Hays Moss +6 more
TL;DR: A methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented and the issue of class imbalance is addressed using various sampling strategies and the classifier generalization error is estimated using Monte Carlo cross validation.
Journal ArticleDOI
Lesion border detection in dermoscopy images.
TL;DR: A systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects is presented.
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Border detection in dermoscopy images using statistical region merging.
M. Emre Celebi,Hassan A. Kingravi,Hitoshi Iyatomi,Y. Alp Aslandogan,William V. Stoecker,Randy Hays Moss,Joseph M. Malters,James M. Grichnik,Ashfaq A. Marghoob,Harold S. Rabinovitz,Scott W. Menzies +10 more
TL;DR: This work has shown that automated border detection is one of the most important steps in the computer‐aided diagnosis of melanoma, because the accuracy of the subsequent steps crucially depends on it.
Journal ArticleDOI
An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm
Hitoshi Iyatomi,Hitoshi Iyatomi,Hiroshi Oka,M. Emre Celebi,Masahiro Hashimoto,Masafumi Hagiwara,Masaru Tanaka,Koichi Ogawa +7 more
TL;DR: An Internet-based melanoma screening system that separates the tumor area from the surrounding skin using highly accurate dermatologist-like tumor area extraction algorithm, and classifies the tumor as melanoma or nevus using a neural network classifier, and presents the diagnosis.
Journal ArticleDOI
Unsupervised border detection in dermoscopy images
M. Emre Celebi,Y. Alp Aslandogan,William V. Stoecker,Hitoshi Iyatomi,Hiroshi Oka,Xiaohe Chen +5 more
TL;DR: Automated border detection is one of the most important steps in the computer‐aided diagnosis of skin cancer procedure as the accuracy of the subsequent steps crucially depends on the accuracyof this step.