Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images
Abstract Detecting skin melanoma in the early stage using dermoscopic images presents a complex challenge due to the inherent variability in images. Utilizing dermatology datasets, the study aimed to develop Automated Diagnostic Systems for early skin cancer detection. Existing methods often struggl...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09938-4 |
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| author | Md Abdullah All Mahmud Sadia Afrin M. F. Mridha Sultan Alfarhood Dunren Che Mejdl Safran |
| author_facet | Md Abdullah All Mahmud Sadia Afrin M. F. Mridha Sultan Alfarhood Dunren Che Mejdl Safran |
| author_sort | Md Abdullah All Mahmud |
| collection | DOAJ |
| description | Abstract Detecting skin melanoma in the early stage using dermoscopic images presents a complex challenge due to the inherent variability in images. Utilizing dermatology datasets, the study aimed to develop Automated Diagnostic Systems for early skin cancer detection. Existing methods often struggle with diverse skin types, cancer stages, and imaging conditions, highlighting a critical gap in reliability and explainability. The novel approach proposed through this research addresses this gap by utilizing a proposed model with advanced layers, including Global Average Pooling, Batch Normalization, Dropout, and dense layers with ReLU and Swish activations to improve model performance. The proposed model achieved accuracies of 95.23% and 96.48% for the two different datasets, demonstrating its robustness, reliability, and strong performance across other performance metrics. Explainable AI techniques such as Gradient-weighted Class Activation Mapping and Saliency Maps offered insights into the model’s decision- making process. These advancements enhance skin cancer diagnostics, provide medical experts with resources for early detection, improve clinical outcomes, and increase acceptance of Deep Learning-based diagnostics in healthcare. |
| format | Article |
| id | doaj-art-951a67de79bb4b76bb2c519472f34ae9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-951a67de79bb4b76bb2c519472f34ae92025-08-20T03:43:11ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-09938-4Explainable deep learning approaches for high precision early melanoma detection using dermoscopic imagesMd Abdullah All Mahmud0Sadia Afrin1M. F. Mridha2Sultan Alfarhood3Dunren Che4Mejdl Safran5Department of Computer Science, American International University-Bangladesh Department of Computer Science and Engineering, World University of BangladeshDepartment of Computer Science, American International University-BangladeshDepartment of Computer Science, College of Computer and Information Sciences, King Saud UniversityDepartment of Electrical Engineering and Computer Science, Texas A&M University-KingsvilleDepartment of Computer Science, College of Computer and Information Sciences, King Saud UniversityAbstract Detecting skin melanoma in the early stage using dermoscopic images presents a complex challenge due to the inherent variability in images. Utilizing dermatology datasets, the study aimed to develop Automated Diagnostic Systems for early skin cancer detection. Existing methods often struggle with diverse skin types, cancer stages, and imaging conditions, highlighting a critical gap in reliability and explainability. The novel approach proposed through this research addresses this gap by utilizing a proposed model with advanced layers, including Global Average Pooling, Batch Normalization, Dropout, and dense layers with ReLU and Swish activations to improve model performance. The proposed model achieved accuracies of 95.23% and 96.48% for the two different datasets, demonstrating its robustness, reliability, and strong performance across other performance metrics. Explainable AI techniques such as Gradient-weighted Class Activation Mapping and Saliency Maps offered insights into the model’s decision- making process. These advancements enhance skin cancer diagnostics, provide medical experts with resources for early detection, improve clinical outcomes, and increase acceptance of Deep Learning-based diagnostics in healthcare.https://doi.org/10.1038/s41598-025-09938-4Dermoscopic imagesDeep learningEarly-stage melanoma detectionExplainable AISwish activation |
| spellingShingle | Md Abdullah All Mahmud Sadia Afrin M. F. Mridha Sultan Alfarhood Dunren Che Mejdl Safran Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images Scientific Reports Dermoscopic images Deep learning Early-stage melanoma detection Explainable AI Swish activation |
| title | Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images |
| title_full | Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images |
| title_fullStr | Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images |
| title_full_unstemmed | Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images |
| title_short | Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images |
| title_sort | explainable deep learning approaches for high precision early melanoma detection using dermoscopic images |
| topic | Dermoscopic images Deep learning Early-stage melanoma detection Explainable AI Swish activation |
| url | https://doi.org/10.1038/s41598-025-09938-4 |
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