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Published in:

Volume 7 Issue 6
June-2020
eISSN: 2349-5162

Unique Identifier

JETIR2006053

Page Number

384-387

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Title

Face skin disease classification based on images

ISSN

2349-5162

Cite This Article

"Face skin disease classification based on images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.384-387, June-2020, Available :http://www.jetir.org/papers/JETIR2006053.pdf

Abstract

Skin issues not only injure physical health but conjointly induce psychological issues, for the most part for patients whose faces are broken down or possibly disgorged. Ill-treatment reasonable devices, most of the person area components complete to acquire convenient clinical pictures of their face skin condition. On the opposite hand, the convolution neural networks (CNNs) have achieved close up to or conceivably superior performance than sufficient contained by the imaging eld. Therefore, this paper studied completely poles apart CNN algorithms for face skin confusion classification support the clinical pictures. First, from XiangyaDerm, which is, to the simplest of our information, China's largest clinical image dataset of skin diseases, we have a predisposition to established a dataset that contains 2656 face pictures happiness to 6 common skin diseases seborrhea disease of the skin (SK), keratosis (AK), acne (ROS), LE (LE), basal cell spiteful ceroplastic Disease (BCC), and epithelial cell malignant neo plastic disease (SCC)].We performed studies exploitation reflection network algorithms to classify these diseases within the dataset and compared the results. Then, we tend to perform studies maltreatment an freelance dataset of constant sickness sorts, however from different body elements, to perform transfer learning on our models. Assessment of the performances, the models that used transfer learning achieved the next average exactness and recalls for pretty much all structures. Within the check dataset, including 388 facial pictures, the simplest model achieved 92.9%, 89.2%, and 84.3% recollects for the autoimmune confusion, BCC, and SK, severally, and also the mean recall and exactness reached 77.0% and 70.8%.

Key Words

Deep learning, CNN, facial skin disease, medical image processing

Cite This Article

"Face skin disease classification based on images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp384-387, June-2020, Available at : http://www.jetir.org/papers/JETIR2006053.pdf

Publication Details

Published Paper ID: JETIR2006053
Registration ID: 233382
Published In: Volume 7 | Issue 6 | Year June-2020
DOI (Digital Object Identifier):
Page No: 384-387
ISSN Number: 2349-5162

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Cite This Article

"Face skin disease classification based on images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp384-387, June-2020, Available at : http://www.jetir.org/papers/JETIR2006053.pdf




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