Open Access

ARTICLE

Enhanced Cutaneous Melanoma Segmentation in Dermoscopic Images Using a Dual U-Net Framework with Multi-Path Convolution Block Attention Module and SE-Res-Conv

Kun Lan¹, Feiyang Gao¹, Xiaoliang Jiang^1,*, Jianzhen Cheng^2,*, Simon Fong³

1 College of Mechanical Engineering, Quzhou University, Quzhou, 324000, China
2 Department of Rehabilitation, Quzhou Third Hospital, Quzhou, 324000, China
3 Department of Computer and Information Science, University of Macau, Macau, 999078, China

* Corresponding Authors: Xiaoliang Jiang. Email: ; Jianzhen Cheng. Email:

(This article belongs to the Special Issue: Multi-Modal Deep Learning for Advanced Medical Diagnostics)

Computers, Materials & Continua 2025, 84(3), 4805-4824. https://doi.org/10.32604/cmc.2025.065864

Received 23 March 2025; Accepted 29 May 2025; Issue published 30 July 2025

Abstract

With the continuous development of artificial intelligence and machine learning techniques, there have been effective methods supporting the work of dermatologist in the field of skin cancer detection. However, object significant challenges have been presented in accurately segmenting melanomas in dermoscopic images due to the objects that could interfere human observations, such as bubbles and scales. To address these challenges, we propose a dual U-Net network framework for skin melanoma segmentation. In our proposed architecture, we introduce several innovative components that aim to enhance the performance and capabilities of the traditional U-Net. First, we establish a novel framework that links two simplified U-Nets, enabling more comprehensive information exchange and feature integration throughout the network. Second, after cascading the second U-Net, we introduce a skip connection between the decoder and encoder networks, and incorporate a modified receptive field block (MRFB), which is designed to capture multi-scale spatial information. Third, to further enhance the feature representation capabilities, we add a multi-path convolution block attention module (MCBAM) to the first two layers of the first U-Net encoding, and integrate a new squeeze-and-excitation (SE) mechanism with residual connections in the second U-Net. To illustrate the performance of our proposed model, we conducted comprehensive experiments on widely recognized skin datasets. On the ISIC-2017 dataset, the IoU value of our proposed model increased from 0.6406 to 0.6819 and the Dice coefficient increased from 0.7625 to 0.8023. On the ISIC-2018 dataset, the IoU value of proposed model also improved from 0.7138 to 0.7709, while the Dice coefficient increased from 0.8285 to 0.8665. Furthermore, the generalization experiments conducted on the jaw cyst dataset from Quzhou People’s Hospital further verified the outstanding segmentation performance of the proposed model. These findings collectively affirm the potential of our approach as a valuable tool in supporting clinical decision-making in the field of skin cancer detection, as well as advancing research in medical image analysis.

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