Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection-Reference-Cited by-同舟云学术

Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection

Published:2019-12-18 Issue: Volume: Page:
ISSN:0266-4720
Container-title:Expert Systems
language:en
Short-container-title:Expert Systems

Author:

Khan Muhammad Attique¹,Sharif Muhammad Imran²,Raza Mudassar²^ORCID,Anjum Almas³,Saba Tanzila⁴,Shad Shafqat Ali⁵

Affiliation:

1. Department of Computer Science HITEC University Pakistan

2. Department of Computer Science COMSATS University Islamabad, Wah Campus Islamabad Pakistan

3. Department of Computer Science University of Wah Pakistan

4. College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

5. Department of Computer Science Luther College Iowa USA

Funder

Artificial Intelligence and Data Analytics (AIDA) Lab, Prince Sultan University, Riyadh, Saudi Arabia

HITEC University

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12497

Reference47 articles.

1. Lesion border detection in dermoscopy images using dynamic programming

2. Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection

3. 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

4. Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks

5. A distinctive approach in brain tumor detection and classification using MRI

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