Early detection of melanoma images using gray level co‐occurrence matrix features and machine learning techniques for effective clinical diagnosis-Reference-Cited by-同舟云学术

Early detection of melanoma images using gray level co‐occurrence matrix features and machine learning techniques for effective clinical diagnosis

Published:2020-10-27 Issue:2 Volume:31 Page:682-694
ISSN:0899-9457
Container-title:International Journal of Imaging Systems and Technology
language:en
Short-container-title:Int J Imaging Syst Technol

Author:

Thiyaneswaran B.¹^ORCID,Anguraj K.¹,Kumarganesh S.²^ORCID,Thangaraj K.³

Affiliation:

1. Department of ECE Sona College of Technology Salem Tamil Nadu India

2. Department of ECE Jayalakshmi Institute of Technology Dharmapuri Tamil Nadu India

3. Department of IT Sona College of Technology Salem Tamil Nadu India

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ima.22514

Reference28 articles.

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3. HaiderS ChoD AmelardR WongA ClausiDA.Enhanced classification of malignant melanoma lesions via the integration of physiological features from dermatological photographs. Paper presented at: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Chicago IL USA; 2017:6455‐6458.

4. Instructional scaffolding for ASIP design education with System Verilog assertion considering situated nature of learning;Ryuichi T;Int J Comput Sci Network Sec,2016

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