Bridging the Black Box: An Interpretable Deep Learning Framework for Multi-Class Dermatological Screening
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2026
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| _version_ | 1866901544558919680 |
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| author | Mr. Aniket Kumar Mr. Pawan Kumar |
| author_facet | Mr. Aniket Kumar Mr. Pawan Kumar |
| contents | <p class="MsoNormal"><strong><span>Abstract</span></strong></p> <p class="MsoNormal"><span>Skin cancer remains a major clinical concern, and timely assessment of suspicious lesions can improve dermatological decision-making. This paper presents DermaSense AI, a deep learning framework for automated multi-class skin lesion classification on HAM10000. The proposed system combines a two-phase transfer-learning strategy with Xception augmented by a Convolutional</span></p> <p class="MsoNormal"><span>Block Attention Module (CBAM) and DenseNet201. A weighted soft-voting ensemble achieves a macro Area Under the Curve (AUC) of 95.92% and an overall accuracy of 80.24% on the held-out test set. To support qualitative interpretation, Gradientweighted Class Activation Mapping (Grad-CAM) is used to visualize model attention. In addition, a dedicated melanoma-screening analysis attains a Negative Predictive Value (NPV) of 97.1% at the selected operating threshold, highlighting the potential of the framework as a decision-support aid rather than a standalone diagnostic system.</span></p> <p class="MsoNormal"><strong><span>Keywords:</span></strong><span> Skin Lesion Classification, Deep Learning, CBAM Attention, Ensemble Learning, Grad-CAM, HAM10000, Clinical Triage</span></p> <p class="MsoNormal"><span> </span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20259265 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Bridging the Black Box: An Interpretable Deep Learning Framework for Multi-Class Dermatological Screening Mr. Aniket Kumar Mr. Pawan Kumar <p class="MsoNormal"><strong><span>Abstract</span></strong></p> <p class="MsoNormal"><span>Skin cancer remains a major clinical concern, and timely assessment of suspicious lesions can improve dermatological decision-making. This paper presents DermaSense AI, a deep learning framework for automated multi-class skin lesion classification on HAM10000. The proposed system combines a two-phase transfer-learning strategy with Xception augmented by a Convolutional</span></p> <p class="MsoNormal"><span>Block Attention Module (CBAM) and DenseNet201. A weighted soft-voting ensemble achieves a macro Area Under the Curve (AUC) of 95.92% and an overall accuracy of 80.24% on the held-out test set. To support qualitative interpretation, Gradientweighted Class Activation Mapping (Grad-CAM) is used to visualize model attention. In addition, a dedicated melanoma-screening analysis attains a Negative Predictive Value (NPV) of 97.1% at the selected operating threshold, highlighting the potential of the framework as a decision-support aid rather than a standalone diagnostic system.</span></p> <p class="MsoNormal"><strong><span>Keywords:</span></strong><span> Skin Lesion Classification, Deep Learning, CBAM Attention, Ensemble Learning, Grad-CAM, HAM10000, Clinical Triage</span></p> <p class="MsoNormal"><span> </span></p> |
| title | Bridging the Black Box: An Interpretable Deep Learning Framework for Multi-Class Dermatological Screening |
| url | https://doi.org/10.5281/zenodo.20259265 |