SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare
<b>Background:</b> Skin cancer, particularly melanoma, poses significant challenges due to the heterogeneity of skin images and the demand for accurate and interpretable diagnostic systems. Early detection and effective management are crucial for improving patient outcomes. Traditional A...
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MDPI AG
2024-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/1/64 |
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| author | Sultanul Arifeen Hamim Mubasshar U. I. Tamim M. F. Mridha Mejdl Safran Dunren Che |
| author_facet | Sultanul Arifeen Hamim Mubasshar U. I. Tamim M. F. Mridha Mejdl Safran Dunren Che |
| author_sort | Sultanul Arifeen Hamim |
| collection | DOAJ |
| description | <b>Background:</b> Skin cancer, particularly melanoma, poses significant challenges due to the heterogeneity of skin images and the demand for accurate and interpretable diagnostic systems. Early detection and effective management are crucial for improving patient outcomes. Traditional AI models often struggle with balancing accuracy and interpretability, which are critical for clinical adoption. <b>Methods:</b> The SmartSkin-XAI methodology incorporates a fine-tuned DenseNet121 model combined with XAI techniques to interpret predictions. This approach improves early detection and patient management by offering a transparent decision-making process. The model was evaluated using two datasets: the ISIC dataset and the Kaggle dataset. Performance metrics such as classification accuracy, precision, recall, and F1 score were compared against benchmark models, including DenseNet121, InceptionV3, and esNet50. <b>Results:</b> SmartSkin-XAI achieved a classification accuracy of 97% on the ISIC dataset and 98% on the Kaggle dataset. The model demonstrated high stability in precision, recall, and F1 score measures, outperforming the benchmark models. These results underscore the robustness and applicability of SmartSkin-XAI for real-world healthcare scenarios. <b>Conclusions:</b> SmartSkin-XAI addresses critical challenges in melanoma diagnosis by integrating state-of-the-art architecture with XAI methods, providing both accuracy and interpretability. This approach enhances clinical decision-making, fosters trust among healthcare professionals, and represents a significant advancement in incorporating AI-driven diagnostics into medicine, particularly for bedside applications. |
| format | Article |
| id | doaj-art-4b2db7c6646d4242beaab5ab15c20ed2 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-4b2db7c6646d4242beaab5ab15c20ed22025-01-10T13:16:36ZengMDPI AGDiagnostics2075-44182024-12-011516410.3390/diagnostics15010064SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart HealthcareSultanul Arifeen Hamim0Mubasshar U. I. Tamim1M. F. Mridha2Mejdl Safran3Dunren Che4Department of Computer Science, American International University-Bangladesh, Dhaka 1229, BangladeshDepartment of Computer Science, American International University-Bangladesh, Dhaka 1229, BangladeshDepartment of Computer Science, American International University-Bangladesh, Dhaka 1229, BangladeshDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi ArabiaDepartment of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX 78363, USA<b>Background:</b> Skin cancer, particularly melanoma, poses significant challenges due to the heterogeneity of skin images and the demand for accurate and interpretable diagnostic systems. Early detection and effective management are crucial for improving patient outcomes. Traditional AI models often struggle with balancing accuracy and interpretability, which are critical for clinical adoption. <b>Methods:</b> The SmartSkin-XAI methodology incorporates a fine-tuned DenseNet121 model combined with XAI techniques to interpret predictions. This approach improves early detection and patient management by offering a transparent decision-making process. The model was evaluated using two datasets: the ISIC dataset and the Kaggle dataset. Performance metrics such as classification accuracy, precision, recall, and F1 score were compared against benchmark models, including DenseNet121, InceptionV3, and esNet50. <b>Results:</b> SmartSkin-XAI achieved a classification accuracy of 97% on the ISIC dataset and 98% on the Kaggle dataset. The model demonstrated high stability in precision, recall, and F1 score measures, outperforming the benchmark models. These results underscore the robustness and applicability of SmartSkin-XAI for real-world healthcare scenarios. <b>Conclusions:</b> SmartSkin-XAI addresses critical challenges in melanoma diagnosis by integrating state-of-the-art architecture with XAI methods, providing both accuracy and interpretability. This approach enhances clinical decision-making, fosters trust among healthcare professionals, and represents a significant advancement in incorporating AI-driven diagnostics into medicine, particularly for bedside applications.https://www.mdpi.com/2075-4418/15/1/64melanoma detectionexplainable AIdeep learningDenseNetSmartSkin-XAI |
| spellingShingle | Sultanul Arifeen Hamim Mubasshar U. I. Tamim M. F. Mridha Mejdl Safran Dunren Che SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare Diagnostics melanoma detection explainable AI deep learning DenseNet SmartSkin-XAI |
| title | SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare |
| title_full | SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare |
| title_fullStr | SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare |
| title_full_unstemmed | SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare |
| title_short | SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare |
| title_sort | smartskin xai an interpretable deep learning approach for enhanced skin cancer diagnosis in smart healthcare |
| topic | melanoma detection explainable AI deep learning DenseNet SmartSkin-XAI |
| url | https://www.mdpi.com/2075-4418/15/1/64 |
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