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Efficacy Check of Haralick and Symmetry features for Skin Lesions Classification

by Adwait Laud, Shruti Borkar, Shrijanya Rai, Udit Kalra, Dhirendra Mishra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 3
Year of Publication: 2023
Authors: Adwait Laud, Shruti Borkar, Shrijanya Rai, Udit Kalra, Dhirendra Mishra
10.5120/ijca2023922679

Adwait Laud, Shruti Borkar, Shrijanya Rai, Udit Kalra, Dhirendra Mishra . Efficacy Check of Haralick and Symmetry features for Skin Lesions Classification. International Journal of Computer Applications. 185, 3 ( Apr 2023), 1-8. DOI=10.5120/ijca2023922679

@article{ 10.5120/ijca2023922679,
author = { Adwait Laud, Shruti Borkar, Shrijanya Rai, Udit Kalra, Dhirendra Mishra },
title = { Efficacy Check of Haralick and Symmetry features for Skin Lesions Classification },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2023 },
volume = { 185 },
number = { 3 },
month = { Apr },
year = { 2023 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number3/32682-2023922679/ },
doi = { 10.5120/ijca2023922679 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:25:08.449918+05:30
%A Adwait Laud
%A Shruti Borkar
%A Shrijanya Rai
%A Udit Kalra
%A Dhirendra Mishra
%T Efficacy Check of Haralick and Symmetry features for Skin Lesions Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 3
%P 1-8
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Skin cancer is growing on a very fast pace globally. There is a to develop an approach for early detection of skin cancer. Numerous approaches have been used to detect skin lesions using image processing and deep learning techniques. This paper experiments to investigate the results of Machine learning algorithms and ResNet18 Model based on the various inherent features extracted and classify images in the HAMS 10000 database. The accuracy of ResNet18 model on HAMS 10000 dataset is 85 %. The features Correlation, Homogeneity, Energy, Contrast and ASM are extracted from the skin lesions and is classified using Grey Level Co-occurrence Matrices (GLCM) and combined with features asymmetry index, compact index, standard deviation of red, blue and green pixels, lesion-diameter are combined with the features and then are passed to SVM classifier and obtains an average accuracy of 67.5%. Whereas it is found that the combination of SMF features along with Haralick features gives overall best accuracy of 70.2% using Random Forest classifier. Thereby neural networks gives better results than machine learning approaches for lesion classification.

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Index Terms

Computer Science
Information Sciences

Keywords

GLCM Skin Cancer SVM Classifier ResNet18 Feature extraction

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