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A Convolutional Neural Network for Skin Lesion Segmentation Using Double U-Net Architecture

Iqra Abid1, Sultan Almakdi2, Hameedur Rahman3, Ahmed Almulihi4, Ali Alqahtani2, Khairan Rajab2,5, Abdulmajeed Alqhatani2,*, Asadullah Shaikh2

1 Institute of Southern Punjab, Multan, 32100, Pakistan
2 College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
3 Department of Creative Technology, Air University, Islamabad, 44200, Pakistan
4 Department of Computer Science College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
5 College of Computer Science and Engineering, University of South Florida, Tampa, 33620, United States

* Corresponding Author: Abdulmajeed Alqhatani. Email: email

Intelligent Automation & Soft Computing 2022, 33(3), 1407-1421.


Skin lesion segmentation plays a critical role in the precise and early detection of skin cancer via recent frameworks. The prerequisite for any computer-aided skin cancer diagnosis system is the accurate segmentation of skin malignancy. To achieve this, a specialized skin image analysis technique must be used for the separation of cancerous parts from important healthy skin. This procedure is called Dermatography. Researchers have often used multiple techniques for the analysis of skin images, but, because of their low accuracy, most of these methods have turned out to be at best, inconsistent. Proper clinical treatment involves sensitivity in the surgical process. A high accuracy rate is therefore of paramount importance. A generalized and robust model is needed to accurately assess and segment skin lesions. In this regard, a novel approach named Double U-Net has been proposed to provide necessary strength and Robustness. This process uses two U-Net architectures stacked upon each other with ASPP which is used to squeeze out a high resolution and redundant information. In this paper, we trained the proposed architecture on the PH2 dataset and the model was evaluated on the PH2 test, ISIC-2016 and HAM datasets. Evaluation of information shows the model achieved a DSC of 0.9551 on the PH2 test dataset, 0.8104 on ISIC-2016 and 0.7645 on the HAM dataset. Analyses show results comparable to the most recently available state-of-the-art techniques.


Cite This Article

I. Abid, S. Almakdi, H. Rahman, A. Almulihi, A. Alqahtani et al., "A convolutional neural network for skin lesion segmentation using double u-net architecture," Intelligent Automation & Soft Computing, vol. 33, no.3, pp. 1407–1421, 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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