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A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification

Javaria Tahir1, Syed Rameez Naqvi2,*, Khursheed Aurangzeb3, Musaed Alhussein3

1 Department of Computer Science, COMSATS University Islamabad, Islamabad Campus, Pakistan
2 Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Pakistan
3 Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

* Corresponding Author: Syed Rameez Naqvi. Email: email

(This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)

Computers, Materials & Continua 2022, 70(2), 3235-3250. https://doi.org/10.32604/cmc.2022.018949

Abstract

Melanoma, due to its higher mortality rate, is considered as one of the most pernicious types of skin cancers, mostly affecting the white populations. It has been reported a number of times and is now widely accepted, that early detection of melanoma increases the chances of the subject’s survival. Computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques. In this work, we propose a framework that accurately segments, and later classifies, the lesion using improved image segmentation and fusion methods. The proposed technique takes an image and passes it through two methods simultaneously; one is the weighted visual saliency-based method, and the second is improved HDCT based saliency estimation. The resultant image maps are later fused using the proposed image fusion technique to generate a localized lesion region. The resultant binary image is later mapped back to the RGB image and fed into the Inception-ResNet-V2 pre-trained model–trained by applying transfer learning. The simulation results show improved performance compared to several existing methods.

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Cite This Article

J. Tahir, S. Rameez Naqvi, K. Aurangzeb and M. Alhussein, "A saliency based image fusion framework for skin lesion segmentation and classification," Computers, Materials & Continua, vol. 70, no.2, pp. 3235–3250, 2022.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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