International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 77 - 84

Multi-Class Skin Lesions Classification System Using Probability Map Based Region Growing and DCNN

Authors
T. Sreekesh Namboodiri1, A. Jayachandran2, *
1Research Scholar, Department of CSE, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India
2Department of CSE, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India Zipcode-627152
*Corresponding author. Email: ajaya1675@gmail.com
Corresponding Author
A. Jayachandran
Received 13 September 2019, Accepted 23 December 2019, Available Online 22 January 2020.
DOI
10.2991/ijcis.d.200117.002How to use a DOI?
Keywords
Black frame removal; Gaussian filtering; Region growing; Optimal thresholding; Geometric features; SVM classification
Abstract

Background:

Melanoma is a type of threatening pigmented skin lesion, and as of now is among the most hazardous existing diseases. Suitable automated diagnosis of skin lesions and Melanoma classification can extraordinarily enhance early identification of melanomas.

Methods:

However, classification models based on deterministic skin lesion can influence multi-dimensional nonlinear problem which leads to inaccurate and inefficient classification. This paper presents a Deep Convolutional Neural Network (DCNN) classification approach for segmented skin lesions in dermoscopy images. As an initial step, the skin lesion is preprocessed by an automatic preprocessing algorithm together with a fusion hair detection and removal strategy. Also a new probability map based region growing and optimal thresholding algorithm is integrated in our system which yields tremendous accuracy.

Results:

For obtaining more prominent results a set of features containing ABCD features as well as geometric features are calculated in the feature extraction step to describe the malignancy of the lesion.

Conclusions:

The experimental result shows that the system is efficient and works well on dermoscopy images, achieving considerable accuracy.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
77 - 84
Publication Date
2020/01/22
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200117.002How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - T. Sreekesh Namboodiri
AU  - A. Jayachandran
PY  - 2020
DA  - 2020/01/22
TI  - Multi-Class Skin Lesions Classification System Using Probability Map Based Region Growing and DCNN
JO  - International Journal of Computational Intelligence Systems
SP  - 77
EP  - 84
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200117.002
DO  - 10.2991/ijcis.d.200117.002
ID  - Namboodiri2020
ER  -