aceAn Explainable Computer-Aided Framework for Skin Lesion Classification Using Deep Learning
The utilization of deep convolutional neural networks for the purpose of diagnosing diseases in the skin area has proven to yield similar accuracy levels to those obtained by dermatologists in different studies. Nevertheless, many challenges are still present, including underperformance and poor generalization in some cases, as well as low interpretability related to the use of black box models. This creates major obstacles for practical implementation since it requires explainability in addition to accurate dia…
A three-stage explainable framework that fuses deep representations from multiple pretrained CNNs and classifies them with an RBF-kernel SVM achieved 88% accuracy and 88% F1 for skin lesion classification while adding LIME-based explanations.
Performance claims are bounded to the specific experimental setup and comparison set used, with acknowledged risks of underperformance and poor generalization outside those conditions.
Evidence
- Peer-reviewedJournal of Visualized Experiments2026-08-25
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Truvace Impact Record TRV-2026-0905, v1: “An Explainable Computer-Aided Framework for Skin Lesion Classification Using Deep Learning.” Truvace, 2026-08-27. /record/TRV-2026-0905 (accessed at citation time). sha256 40fe80f10335994f…
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