Dual Focus Attention Block UNet (DFAB-UNet) for Robust Skin Lesion Segmentation

Authors

Fallah H. Najjar, Department of Emergent Computing, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia AND Department of Computer Networks and Software Techniques, Technical Institute of Najaf, Al-Furat Al-Awsat Technical University, 54001 Najaf, IraqFollow
Farhan Mohamed, Department of Emergent Computing, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia AND Media and Game Innovation Center of Excellence, Institute of Human Centered Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, MalaysiaFollow
Mohd Shafry Mohd Rahim, Department of Emergent Computing, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, MalaysiaFollow
Diana Sofia de Oliveira Bernardo, Champalimaud Centre for the Unknown, 1400-038 Lisbon, PortugalFollow
Luís Filipe Almeida Bernardo, GeoBioTec, Department of Civil Engineering and Architecture, University of Beira Interior, 6201-001 Covilhã, PortugalFollow
Vei Siang Chan, Department of Emergent Computing, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia AND Media and Game Innovation Center of Excellence, Institute of Human Centered Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, MalaysiaFollow

Abstract

Skin cancer is the most fatal type of cancer worldwide. Prognosis is generally much better with early detection and effective treatment relies on an accurate diagnosis of skin lesions. Despite progress in deep learning, there is still a challenge to provide accurate segmentation of dermoscopic skin lesions due to image variations stemming from different illuminative input parameters, different resolutions/image sizes, image artifacts and varying skins. These differences hinder state-of-the-art models from learning pixel-precise lesion boundaries or more contextual features, consequently impairing their generalization and clinical applicability. We introduce a new architecture called Dual Focus Attention Block UNet (DFAB-UNet) with intrinsic capability of local and global feature extraction tasks. We trained and tested the proposed method on two commonly used sets of dermoscopy images, PH2 and HAM10000 with respect to six different parameters, namely, Accuracy (Acc), Precision (Pre), Sensitivity (Sen), Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and specificity (Spe) for comparative analysis Results. DFAB-UNET achieved an Acc of 97.24%, a DSC of 94.30%, an IoU of 89.21% and a Spe of 98.95% on the PH2 dataset. On the HAM10000, we achieved an Acc of 96.20%, DSC of 92.79%, IoU of 86.55% and Spe of 97.60%. These results show that the model generalizes across datasets by introducing boundary precision represented in the loss together with contextual lesion information. The DFAB-UNet model achieves high performance on segmentation, indicating the potential use and application of dermal disease research in clinic practice.

Keywords

DFAB-UNet, Medical image segmentation, Skin cancer, Skin lesion segmentation, UNet

Subject Area

Computer Science

Article Type

Article

First Page

1100

Last Page

1115

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite this Article

Najjar, Fallah H.; Mohamed, Farhan; Rahim, Mohd Shafry Mohd; Bernardo, Diana Sofia de Oliveira; Bernardo, Luís Filipe Almeida; and Chan, Vei Siang (2026) "Dual Focus Attention Block UNet (DFAB-UNet) for Robust Skin Lesion Segmentation," Baghdad Science Journal: Vol. 23: Iss. 3, Article 30.
DOI: https://doi.org/10.21123/2411-7986.5233