An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self-attention mechanisms
The skin, the body's largest organ, plays a critical role in protection and regulation, making its health essential. Skin cancer, one of the most prevalent malignancies, continues to rise globally and presents significant risks when diagnosis is delayed. Accurate detection is challenging due to...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024019352 |
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| author | Burhanettin Ozdemir Ishak Pacal |
| author_facet | Burhanettin Ozdemir Ishak Pacal |
| author_sort | Burhanettin Ozdemir |
| collection | DOAJ |
| description | The skin, the body's largest organ, plays a critical role in protection and regulation, making its health essential. Skin cancer, one of the most prevalent malignancies, continues to rise globally and presents significant risks when diagnosis is delayed. Accurate detection is challenging due to the subtle and overlapping features of skin lesions, often leading to diagnostic errors. Deep learning has emerged as a powerful tool, capable of analyzing complex dermatological data and improving diagnostic accuracy through precise pattern recognition. This study proposes a novel lightweight and mobile-friendly hybrid model that combines ConvNeXtV2 blocks and focal self-attention mechanisms, addressing challenges such as data imbalance and model complexity. The Proposed Model employs ConvNeXtV2 in the first two stages for superior local feature extraction, while focal self-attention in the subsequent stages enhances sensitivity by focusing on diagnostically relevant regions. The Proposed Model was evaluated on the ISIC 2019 dataset, encompassing eight skin cancer classes with significant class imbalances, such as the Melanocytic Nevus class having 51 times more images than the Vascular Lesion class. Despite these disparities, the Proposed Model achieved robust performance across all classes, with 93.60% accuracy, 91.69% precision, 90.05% recall, and a 90.73% F1-score. Compared to baseline models and existing literature, it demonstrated a 10.8% improvement in accuracy over ResNet50 and a 3.3% improvement over the best-performing vision transformer (Swinv2-Base). This innovative design establishes a new benchmark in skin cancer detection, offering accurate, scalable, and generalizable predictions to support early diagnosis and improved clinical outcomes. |
| format | Article |
| id | doaj-art-6ae74623a1844aaabda417d4c36f878f |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-6ae74623a1844aaabda417d4c36f878f2025-08-20T02:39:51ZengElsevierResults in Engineering2590-12302025-03-012510369210.1016/j.rineng.2024.103692An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self-attention mechanismsBurhanettin Ozdemir0Ishak Pacal1Department of Operations and Project Management, College of Business, Alfaisal University, 11533, Riyadh, Saudi Arabia; Corresponding author.Department of Computer Engineering, Faculty of Engineering, Igdir University, 76000, Igdir, TurkeyThe skin, the body's largest organ, plays a critical role in protection and regulation, making its health essential. Skin cancer, one of the most prevalent malignancies, continues to rise globally and presents significant risks when diagnosis is delayed. Accurate detection is challenging due to the subtle and overlapping features of skin lesions, often leading to diagnostic errors. Deep learning has emerged as a powerful tool, capable of analyzing complex dermatological data and improving diagnostic accuracy through precise pattern recognition. This study proposes a novel lightweight and mobile-friendly hybrid model that combines ConvNeXtV2 blocks and focal self-attention mechanisms, addressing challenges such as data imbalance and model complexity. The Proposed Model employs ConvNeXtV2 in the first two stages for superior local feature extraction, while focal self-attention in the subsequent stages enhances sensitivity by focusing on diagnostically relevant regions. The Proposed Model was evaluated on the ISIC 2019 dataset, encompassing eight skin cancer classes with significant class imbalances, such as the Melanocytic Nevus class having 51 times more images than the Vascular Lesion class. Despite these disparities, the Proposed Model achieved robust performance across all classes, with 93.60% accuracy, 91.69% precision, 90.05% recall, and a 90.73% F1-score. Compared to baseline models and existing literature, it demonstrated a 10.8% improvement in accuracy over ResNet50 and a 3.3% improvement over the best-performing vision transformer (Swinv2-Base). This innovative design establishes a new benchmark in skin cancer detection, offering accurate, scalable, and generalizable predictions to support early diagnosis and improved clinical outcomes.http://www.sciencedirect.com/science/article/pii/S2590123024019352Medical image analysisSkin cancer detectionConvNeXtv2Vision transformerFocal self-attention |
| spellingShingle | Burhanettin Ozdemir Ishak Pacal An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self-attention mechanisms Results in Engineering Medical image analysis Skin cancer detection ConvNeXtv2 Vision transformer Focal self-attention |
| title | An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self-attention mechanisms |
| title_full | An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self-attention mechanisms |
| title_fullStr | An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self-attention mechanisms |
| title_full_unstemmed | An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self-attention mechanisms |
| title_short | An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self-attention mechanisms |
| title_sort | innovative deep learning framework for skin cancer detection employing convnextv2 and focal self attention mechanisms |
| topic | Medical image analysis Skin cancer detection ConvNeXtv2 Vision transformer Focal self-attention |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024019352 |
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