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BMC Cancer
Published in: BMC Cancer 1/2018

Open Access 01-12-2018 | Research article

An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification

Authors: M. Attique Khan, Tallha Akram, Muhammad Sharif, Aamir Shahzad, Khursheed Aurangzeb, Musaed Alhussein, Syed Irtaza Haider, Abdualziz Altamrah

Published in: BMC Cancer | Issue 1/2018

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Abstract

Background

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency.

Methods

In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier.

Results

The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively.

Conclusion

The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets.
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Metadata
Title
An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification
Authors
M. Attique Khan
Tallha Akram
Muhammad Sharif
Aamir Shahzad
Khursheed Aurangzeb
Musaed Alhussein
Syed Irtaza Haider
Abdualziz Altamrah
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2018
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-018-4465-8

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