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1. A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer

Description:

Skin Cancer is one of the most widespread forms of cancer in the world which can be detected using dermatoscopic images. In this paper, a texture based feature extraction algorithm is presented for the classification of dermatoscopic images. A median based Local Ternary Pattern is extracted followed by the computation of local quantized ternary patterns. The feature set extracted is then classified using a modified convolutional neural network. The images used for the detection of multiple types of skin cancer are obtained from two publicly available datasets, HAM10000 and ISICUDA11. For the proposed technique, the average recall value, average precision and average accuracy is found to be 75.20%, 95.44% and 96% respectively. An average increase in accuracy for the proposed algorithm is up-to 50.6%, 24.1% and 4.7% over LTP, DLTerQEP and a DE ANN based algorithm respectively.

Keyword:

Medical imaging, Image classification, Image retrieval, and Texture detection

Subject:

Medical Imaging, Data Science, Radiodiagnosis, Computer Science, Artificial Intelligence, and Radiology

Creator:

Sudipta Roy , Varun Srivastava, and Deepika Kumar

Contributor:

Artificial Intelligence and Data Science, Jio Institute, Navi Mumbai, India

Owner:

n.sakthivel@jioinstitute.edu.in

Publisher:

Elsevier

Location:

United States

Language:

English

Date Uploaded:

07-02-2023

Date Modified:

16-02-2023

Date Created:

01-09-2022

Rights Statement Tesim:

In Copyright

License Tesim:

All rights reserved

Resource Type:

Article

Identifier:

10.1016/j.compeleceng.2022.108259

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1. A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer

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1. A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer

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