http://iet.metastore.ingenta.com
1887

Deep residual network with regularised fisher framework for detection of melanoma

Deep residual network with regularised fisher framework for detection of melanoma

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Of all the skin cancer that is prevalent, melanoma has the highest mortality rates. Melanoma becomes life threatening when it penetrates deep into the dermis layer unless detected at an early stage, it becomes fatal since it has a tendency to migrate to other parts of our body. This study presents an automated non-invasive methodology to assist the clinicians and dermatologists for detection of melanoma. Unlike conventional computational methods which require (expensive) domain expertise for segmentation and hand crafted feature computation and/or selection, a deep convolutional neural network-based regularised discriminant learning framework which extracts low-dimensional discriminative features for melanoma detection is proposed. Their approach minimises the whole of within-class variance information and maximises the total class variance information. The importance of various subspaces arising in the within-class scatter matrix followed by dimensionality reduction using total class variance information is analysed for melanoma detection. Experimental results on ISBI 2016, MED-NODE, PH2 and the recent ISBI 2017 databases show the efficacy of their proposed approach as compared to other state-of-the-art methodologies.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2018.5238
Loading

Related content

content/journals/10.1049/iet-cvi.2018.5238
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address