1. accuracy 3. Method Name: Deep Learning and Convolutional

1.
Method name: Deep learning using MatConvNet

Paper
name: Skin Lesion Analysis towards Melanoma Detection Using Deep Learning
Network

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Author:
Yuexiang Li and Linlin Shen

Year:
2017

Description
: In this paper  they proposed   two deep learning methods to address all the
three tasks announced in ISIC 2017, i.e. lesion segmentation (task 1), lesion
dermoscopic feature extraction (task 2) and lesion classification (task 3). The
proposed deep learning frameworks were evaluated on the ISIC 2017 testing set.
Experimental results show the accuracies of frameworks, i.e. 0.718 for task 1,
0.833 for task 2 and 0.823 for task 3 were achieved.

2.
Method Name: Deep Learning Technique with the VGG16, VGG19 and GoogleNet  models

Paper
Name: Deep Learning Approach to Universal Skin Disease Classification

Author:  Haofu Liao

Year:
2016

Description:
Skin diseases are very common in people’s daily life. In this paper, we
investigate the   feasibility of
constructing a universal skin disease diagnosis system using deep convolutional
neural network. They took dermenet dataset and test its performance with both
the Dermenet and OLE, another skin disease dataset, images. This system can
achieve as high as 73.1% accuracy

3.
Method Name: Deep Learning and Convolutional Neural Networks using ResNet Model

Paper
name:  Deep Learning and Convolutional
Neural Networks in the Aid of the Classification of Melanoma

Author:
Felipe Moure C ??cero, Ary Henrique M Oliveira, Glenda Michele Botelho

Year:
2016

Description:  In this paper they had used   transfer learning, convolutional neural networks
and data augmentation of the deep network ResNet (Deep Residual Network)  by taking 
custom  dataset of skin diseases
they had classified classifying whether a melanotic lesion is the malignant
type (melanoma) or not (nevus).

4.
Method Name: Fuzzy and Wavelet Technique

Paper
Name:  Analysis of Skin Cancer Using
Fuzzy and Wavelet Technique

Author:
Nilkamal S. Ramteke and Shweta V. Jain

Year:2013
Description: This paper presents a new approach for Skin Cancer detection and
analysis from given photograph of patient’s cancer affected area, which can be
used to automate the diagnosis and theoretic treatment of skin cancer. In this
paper Wavelet Transformation for image improvement, denoising and Histogram
Analysis .They are proposing to use ABCD rule as its diagnostic accuracy has
been reported to be 76%. A combination of both ABCD rules and wavelet
coefficients has been shown to improve the image feature classification
accuracy by 60%.

5.
Method:  

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