https://mgv.sggw.edu.pl/ <p><strong><em>Machine GRAPHICS &amp; VISION</em></strong> is a refereed international journal, published quarterly by the <a href="https://iit.sggw.edu.pl/?lang=en" target="_blank" rel="noopener">Institute of Information Technology</a> of the <a href="https://www.sggw.edu.pl/en/" target="_blank" rel="noopener">Warsaw University of Life Sciences</a> – <a href="https://www.sggw.edu.pl/en/" target="_blank" rel="noopener">SGGW</a>, in cooperation with the <a href="https://tpo.org.pl/" target="_blank" rel="noopener">Association for Image Processing</a>, Poland – <a href="https://tpo.org.pl/" target="_blank" rel="noopener">TPO</a>.</p> <p><strong><em>MG&amp;V</em></strong> has been published since 1992.</p> <p><strong><em>Machine GRAPHICS &amp; VISION</em></strong> provides a scientific exchange forum and an authoritative source of information in the field of, in general, pictorial information exchange between computers and their environment, including applications of visual and graphical computer systems (<a href="https://czasopisma.sggw.edu.pl/index.php/mgv/about">more</a>).</p> Szkoła Główna Gospodarstwa Wiejskiego w Warszawie en-US Machine Graphics & Vision 1230-0535 https://mgv.sggw.edu.pl/article/view/10239 <p>Skin lesion segmentation identifies and outlines the boundaries of abnormal skin regions. Accurate segmentation may help in the early detection of skin cancer. Accurate Skin Lesion Segmentation is still challenging due to different skin color tones, variations in shape, and body hairs. Moreover, variability in the lesion appearance, quality of the images, and lack of clear skin boundaries make the problem even harder. This paper proposes a SegNet model with spatial attention mechanisms for skin lesion segmentation. Adding one component of spatial attention to SegNet allows the proposed model to focus more on specific parts across the image, eventually leading to a better segmentation of the lesion boundary. The proposed model was evaluated on the ISIC 2018 dataset. Our proposed model attained an average accuracy of 96.25%, and the average dice coefficient equals 0.9052. The model's performance indicates its possible application in automated skin disease diagnosis.</p> Maryam Arif Almas Abbasi Muhammad Arif Muhammad Rashid Copyright (c) 2025 Machine Graphics & Vision 2025-11-05 2025-11-05 34 4 3 22 10.22630/MGV.2025.34.4.1