Open AccessA Novel Light Weight Approach For Identification of Psoriasis Affected Skin Lesion Using Deep Learning.
Author(s) -
T. Arunkumar,
H. S. Jayanna
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2062/1/012017
Subject(s) - psoriasis , dermatology , medicine , psoriatic arthritis , lesion , identification (biology) , nail (fastener) , pathology , materials science , biology , botany , metallurgy
Psoriasis is a skin disorder which affects the people physically, mentally and emotionally. It is characterized as rough elevated scaly skin which is evident from surrounding skin area. There are various types of psoriasis which include plaque psoriasis, nail psoriasis, guttate psoriasis, inverse psoriasis, pustular psoriasis, erythrodermic psoriasis and psoriatic arthritis. The common trend observed is that the people tend to face difficulties in differentiating and tracking the disorder which will worsen the situation of the affected skin. It is essential to keep track of the affected skin for the prognosis of the disorder. In this work, an attempt is made to identify the psoriasis affected area automatically using MobileNet machine learning model which will become an objective tool in accurate identification of the disorder which in turn helps in effective treatment of the disorder.