Machine Learning Approaches to Multi-Class Human Skin Disease Detection
Author(s):
Asim Saket Samd, Souvik Banerjee, Mukund Srivastava
ISSN:
2349-6002
Cite This Article:
Machine Learning Approaches to Multi-Class Human Skin Disease DetectionInternational Journal of Innovative Research in Technology(www.ijirt.org) ,ISSN: 2349-6002 ,Volume 5 ,Issue 11 ,Page(s):474-477 ,April 2019 ,Available :IJIRT147939_PAPER.pdf
Keywords:
Human Cancer, Machine Learning, Skin Disease, dermoscopy, GLCM,
Abstract
Human Cancer is a standout among the most perilous sickness which is fundamentally brought about by hereditary shakiness of various sub-atomic modifications. Among numerous types of human malignant growth, skin disease is the most widely recognized one. To distinguish skin disease at a beginning time we will ponder and break down them through different procedures named as division and highlight extraction. Here, we center harmful melanoma skin disease, (because of the high centralization of Melanoma-Hier we offer our skin, in the dermis layer of the skin) identification. In this, we utilized our ABCD rule dermoscopy innovation for dangerous melanoma skin disease identification. In this framework diverse advance for skin injury portrayal i.e, first the Image Acquisition Technique, pre-handling, division, characterize include for skin Feature Selection decides sore portrayal, characterization strategies. In the Feature extraction by computerized picture handling technique incorporates GLCM and ABCDE highlights and furthermore we utilized DRLBP. Here we proposed the Recurrent Neural Network to group the kind of ailment.
Article Details
Unique Paper ID: 147939

Publication Volume & Issue: Volume 5, Issue 11

Page(s): 474 - 477
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