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A comparative study of features selection for skin lesion detection from dermoscopic images

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Abstract

Melanoma is rare and mainly considered as the dangerous category of skin cancer. Many researchers proposed diverse efficient techniques for melanoma detection. The main focus of this research is: (1) to discuss the traditional clinical methods for diagnosing skin cancer melanoma, and (2) to review the existing researcher’s attempts in response the critical and challenging task is features selection and extraction for skin cancer melanoma detection from dermoscopy images. This research will also be helpful to recognize the research background of skin cancer melanoma detection through image processing techniques. This cannot be done without a broad literature survey. The literature survey was performed keeping the main category as skin cancer melanoma and the survey included articles, journals, and conferences papers. To perform this study, different databases are considered. All of these databases cover medical image processing and technical literature. To conclude the review, some graphs and tables are presented which perform the comparison between existing techniques.

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Abbreviations

DNA:

Deoxyribonucleic acid

UVA:

Ultraviolet A

UVB:

Ultraviolet B

MRI:

Magnetic resonance imaging

OCT:

Optical coherence tomography

CLSM:

Confocal laser scanning microscopy

ELM:

Epiluminescence microscopy

ABCDE:

Asymmetry, border, color, diameter, and evolving

CASH:

Color, architecture, symmetry, and homogeneity

CADx:

Computer-aided diagnosis system

SIFT:

Scale-invariant feature transform

LBP:

Local binary pattern

RGB:

Red, green, blue

HSB:

Hue, saturation, brightness

HSL:

Hue, saturation, lightness

HSV:

Hue, saturation, value

YUV:

Luminance, two chrominance

YCbCr:

Luminance, chrominance

CMYK:

Cyan, magenta, yellow, black

OOP:

Opponent

SIFT:

Scale invariant feature transform

GLCM:

Gray level co-occurrence matrix

GMRF:

Gaussian Markov random field

AR:

Autoregressive

fBm:

Fractional brownian motion

PCA:

Principal component analysis

WPT:

Wavelet packet transform

KNN:

K-nearest neighbor

ANN:

Artificial neural network

SVM:

Support vector machine

MLP:

Multilayer perceptron

RABGLD:

Regional average binary gray level difference co-occurrence matrix

SFTA:

Segmentation based fractal texture analysis

CNN’s:

Convolutional neural networks

DCNNs:

Deep convolutional neural networks

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Acknowledgement

This work is supported by Artificial Intelligence and Data Analytics (AIDA) Lab Prince Sultan University Riyadh Saudi Arabia. Aaaditionally, this work is supported by the School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor Bahru, Malaysia. Moreover, the authors are also grateful for the support of the Department of Computer Science, Lahore College for Women University, Jail Road, Lahore 54000, Pakistan.

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  1. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor Bahru, Malaysia

    Rabia Javed & Mohd Shafry Mohd Rahim

  2. Department of Computer Science, Lahore College for Women University, Jail Road, Lahore, 54000, Pakistan

    Rabia Javed

  3. College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Tanzila Saba & Amjad Rehman

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  1. Rabia Javed
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Javed, R., Rahim, M.S.M., Saba, T. et al. A comparative study of features selection for skin lesion detection from dermoscopic images. Netw Model Anal Health Inform Bioinforma 9, 4 (2020). https://doi.org/10.1007/s13721-019-0209-1

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