Skin Disease Prediction Using Dynamic Testing in Machine Learning

Skin Disease Prediction Using Dynamic Testing in Machine Learning

1 Prof. T.A.Dhumal , 2Miss. Sakshi Shivsharan,3Miss. Aparna Vasmale

4Miss. Pallavi Jadhav,5Miss. Shruti Adat,6Miss. Anushka Chavan

SVERI’s COLLEGE OF ENGINEERING,PANDHARPUR,MAHARASHTRA,INDIA.

1tadhumal@coe.sveri.ac.in, 2 sakshivshivsharan@coep.sveri.ac.in, 3aparnabvasmale@coep.sveri.ac.in,

4pallaviajadhav@coep.sveri.ac.in,5shrutipadat@coep.sveri.ac.in,   6anushkaschavan@coep.sveri.ac.in

Abstract:
One of the most prevalent illnesses in the modern world is skin disease. A small circular or haphazardly shaped area on the patient's skin can be seen as a result of skin disease. When this illness develops into skin cancer, it can occasionally be very dangerous. This article discussed a few deep learning- based methods for extracting features from various skin cancer images, which can then be used with machine learning classifiers to identify the type of skin disease. A transfer learning model is created for our experiments in which a support vector machine is used for classification and the VGG- 16 layer CNN architecture is used to extract 1000 features from the input image.

Keywords: Convolutional Neural Network, Deep Learning, Melanoma, Skin Lesion, Support Vector Machine, Transfer Learning.