Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion

Author:

Khan Muhammad Attique1,Akram Tallha2,Sharif Muhammad3,Saba Tanzila4ORCID,Javed Kashif5,Lali Ikram Ullah6,Tanik Urcun John7,Rehman Amjad8

Affiliation:

1. Department of Computer Science and EngineeringHITEC University Museum Road, Taxila Pakistan

2. Department of Electrical EngineeringCOMSATS University Islamabad Wah Campus Pakistan

3. Department of Computer ScienceCOMSATS University Islamabad Wah Campus Pakistan

4. College of Computer and Information SciencesPrince Sultan University Riyadh SA

5. Department of RoboticsSMME NUST Islamabad Pakistan

6. Department of Computer ScienceUniversity of Gujrat Gujrat Pakistan

7. Computer Science and Information Systems Texas A&M University‐Commerce USA

8. Department of Information SystemsAl Yamamah University Riyadh KSA

Funder

Prince Sultan University Riyadh Saudi Arabia

Machine Learning Research Group

Publisher

Wiley

Subject

Medical Laboratory Technology,Instrumentation,Histology,Anatomy

Reference94 articles.

1. Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears

2. Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears

3. Agarwal A. Issac A. Dutta M. K. Riha K. &Uher V.(2017).Automated skin lesion segmentation using K‐Means clustering from digital dermoscopic images. Paper presented at the Telecommunications and Signal Processing (TSP) 2017 40th International Conference.

4. Skin lesion segmentation and recognition using multichannel saliency estimation and M‐SVM on selected serially fused features;Akram T.;Journal of Ambient Intelligence and Humanized Computing,2018

5. Alfed N. Khelifi F. &Bouridane A.(2016).Improving a bag of words approach for skin cancer detection in dermoscopic images. Paper presented at the Control Decision and Information Technologies (CoDIT) 2016 International Conference.

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