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Deep learning and transfer learning support skin cancer diagnosis via image processing, and adding attention mechanisms can further boost medical image classification accuracy. This work evaluates the effect of multiple attention mechanisms on a pre-trained Xception model for binary detection of benign versus malignant lesions using the HAM10000 dataset.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/skin-cancer-detection-using-transfer-learning-and-deep-attention-mechanisms/372059/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/skin-cancer-detection-using-transfer-learning-and-deep-attention-mechanisms/372059.png","ImageObject",300,407,{"name":92,"@type":93},"Logic","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-24","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"Why is early and accurate skin cancer detection important?","Question",{"text":112,"@type":113},"Early detection improves survival rates. 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Skin Cancer Detection Using Transfer Learning and Deep Attention Mechanisms. Diagnostics 2025, 15, 99 .  \n[https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)diagnostics15010099  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \nCollege of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia; [dalsaeed@ksu.edu.sa](dalsaeed@ksu.edu.sa)  \n* Correspondence: [areej.400400a@gmail.com](areej.400400a@gmail.com)  \nAbstract: Background/Objectives: Early and accurate diagnosis of skin cancer improves survival rates; however, dermatologists often struggle with lesion detection due to similar pigmentation. Deep learning and transfer learning models have shown promise in diagnosing skin cancers through image processing. Integrating attention mechanisms (AMs) with deep learning has further enhanced the accuracy of medical image classification. While significant progress has been made, further research is needed to improve the detection accuracy. Previous studies have not explored the integration of attention mechanisms with the pre-trained Xception transfer learning model for binary classification of skin cancer. This study aims to investigate the impact of various attention mechanisms on the Xception model’s performance in detecting benign and malignant skin lesions. Methods: We conducted four experiments on the HAM10000 dataset. Three models integrated self-attention (SL), hard attention (HD), and soft attention (SF) mechanisms, while the fourth model used the standard Xception without attention mechanisms. Each mechanism analyzed features from the Xception model uniquely: self-attention examined the input relationships, hard-attention selected elements sparsely, and soft-attention distributed the focus probabilistically. Results: Integrating AMs into the Xception architecture effectively enhanced its performance. The accuracy of the Xception alone was 91.05% . With AMs, the accuracy increased to 94.11% using self-attention, 93.29% with soft attention, and 92.97% with hard attention. Moreover, the proposed models outperformed previous studies in terms of therecall metrics, which are crucial for medical investigations. Conclusions: These findings suggest that AMs can enhance performance in relation to complex medical imaging tasks, potentially supporting earlier diagnosis and improving treatment outcomes.  \nKeywords: skin cancer; deep learning; transfer learning; attention mechanism; computer vision; pre-trained models; Xception; dermoscopic images; medical imaging  \n1. Introduction  \nSkin cancer is one of the most devastating cancers of the present decade, and it is the fifth most common form of cancer [1] . According to the World Health Organization (WHO), melanoma, its most aggressive form, affects over 132,000 new patients worldwide each year [2] . In the United States, skin cancers constitute 4% of all malignant neoplasms, with approximately 1% of all cancer-related deaths attributed to this disease [3] . Within our region, the latest Saudi Cancer Registry showed that skin cancer ranked as the ninth most common cancer among both sexes, representing 3.2% of all newly diagnosed cases in 2010 [4] . Despite this, there is a significant chance in terms of survival of skin cancer when it is caught at an early stage. According to the Centers for Disease Control and Prevention (CDC), the treatment","cbCaitDrhFyn0RiZ","https://ap.wps.com/l/cbCaitDrhFyn0RiZ","pdf",2863921,21,"English","# Abstract\n# 1. Introduction\n## 1.1 Skin cancer burden and epidemiology\n## 1.2 Pathogenesis and risk factors","[{\"question\":\"Why is early and accurate skin cancer detection important?\",\"answer\":\"Early detection improves survival rates. Delayed diagnosis can reduce treatment effectiveness, and early-stage lesions are harder to distinguish visually.\"},{\"question\":\"How does the study incorporate attention mechanisms into the Xception model?\",\"answer\":\"It runs four experiments on the HAM10000 dataset: three Xception-based models with self-attention, hard attention, and soft attention, and one baseline Xception model without attention.\"},{\"question\":\"What performance improvements are reported with attention mechanisms?\",\"answer\":\"The baseline Xception achieves 91.05% accuracy. Adding attention increases accuracy to 94.11% (self-attention), 93.29% (soft attention), and 92.97% (hard attention).\"},{\"question\":\"What does the paper claim attention mechanisms help with in medical imaging?\",\"answer\":\"The results indicate attention mechanisms improve performance on complex medical imaging tasks, which can support earlier diagnosis and better treatment outcomes.\"}]","Skin Cancer Detection Using Transfer Learning and Deep Attention Mechanisms | PDF",1790193612,53]