Open AccessAutomated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
Author(s) -
M. K. Prathiba,
Deepa Jose,
R. Saranya,
Nandhinidevi
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/561/1/012107
Subject(s) - artificial intelligence , computer science , convolutional neural network , pattern recognition (psychology) , residual , segmentation , deep learning , histogram , melanoma diagnosis , artificial neural network , contrast (vision) , image (mathematics) , computer vision , melanoma , medicine , algorithm , cancer research
Automated melanoma recognition using image processing technique from the available dermoscopic images in deep learning is difficult task because of the contrast and variation of melanoma in skin. It is mainly a non-invasive method so that it cannot contact with skin more forcefully. To overcome these disadvantages this research work proposes a method using very deep convolutional neural networks (CNNs). For more accurate classification in this method we are using FCRN and CNN with the effective training limited data. Initially, Performance of Segmentation is done using residual networks using a image from the dataset followed by Classification by neural networks to check the abnormalities in skin. In this kind of classification technique the network has more specified features from the segmented portion alone. The proposed technique is mainly evaluated on datasets and experimental results that would show the performance in histogram and PSNR ratio.