alexa Interpretable Aide Diagnosis System for Melanoma Recognition
ISSN: 2155-9538

Journal of Bioengineering & Biomedical Science
Open Access

Like us on:
OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Research Article

Interpretable Aide Diagnosis System for Melanoma Recognition

Messadi M1*, Ammar M2, Cherifi H1, Chikh MA2 and Bessaid A2

1Laboratoire Electronique, Informatique et Image UMR CNRS 6306, Université de Bourgogne, France

2Biomedical Engineering Laboratory, Department of Electrical and Electronics, Technology Faculty, Abou Bekr Belkaid, Tlemcen University – 13000, Algeria

*Corresponding Author:
Messadi Mahammed
Laboratoire Electronique
Informatique et Image UMR CNRS 6306
Université de Bourgogne, France
Tel: 043202336
E-mail: [email protected]

Received Date: August 10, 2014; Accepted Date: September 10, 2014; Published Date: September 20, 2014

Citation: Messadi M, Ammar M, Cherifi H, Chikh MA, Bessaid A (2014) Interpretable Aide Diagnosis System for Melanoma Recognition. J Bioengineer & Biomedical Sci 4: 132. doi: 10.4172/2155-9538.1000132

Copyright: © 2014 Messadi M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of non-malignant cutaneous diseases. The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention. The aim of this paper is to propose an interpretable classification method for skin tumors in dermoscopic images based on shape descriptors. This work presents a fuzzy rule based classifier to discriminate a melanoma. An adaptive Neuro Fuzzy inference System (ANFIS) is applied in order to discover the fuzzy rules leading to the correct classification. In the first step of the proposed work, we apply the Dullrazor technique to reduce the influence of small structures, hairs, bubbles, light reflexion. In the second step, an unsupervised approach for lesion segmentation is proposed. Iterative thresholding is applied to initialize level set automatically. In this paper, we have also treated the necessity to extract all the specific attributes used to develop a characterization methodology that enables specialists to take the best possible diagnosis. For this purpose, our proposal relies largely on visual observation of the tumor while dealing with some characteristics such as color, texture or form. The method used in this paper is called ABCD. It requires calculating 4 factors: Asymmetry (A), Border (B), Color (C) and Diversity (D). These parameters are used to construct a classification module based on ANFIS for the recognition of malignant melanoma. Finally, we compare the results of classification obtained by ANFIS with SVM (support vector machine) and artificial neural network, and discuss how these results may influence in the following steps: the feature extraction and the final lesion classification. This framework has been tested on a dermoscopic database of 320 images. Experimental results show that the proposed method is effective in improving the interpretability of the fuzzy classifier while preserving the model performances at a satisfactory level.

Keywords

Share This Page

Additional Info

Loading
Loading Please wait..
 
Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri, Food, Aqua and Veterinary Science Journals

Dr. Krish

[email protected]

1-702-714-7001 Extn: 9040

Clinical and Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemical Engineering and Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001 Extn: 9040

Earth & Environmental Sciences

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

General Science and Health care Journals

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics and Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001 Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Informatics Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Mathematics and Physics Journals

Jim Willison

[email protected]

1-702-714-7001 Extn: 9042

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001 Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001 Extn: 9042

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords