Open Access

ARTICLE

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

Iqra Abid¹, Sultan Almakdi², Hameedur Rahman³, Ahmed Almulihi⁴, Ali Alqahtani², Khairan Rajab^2,5, Abdulmajeed Alqhatani^2,*, 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:

Intelligent Automation & Soft Computing 2022, 33(3), 1407-1421. https://doi.org/10.32604/iasc.2022.023753

Received 20 September 2021; Accepted 20 December 2021; Issue published 24 March 2022

Abstract

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.

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