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<p>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.</p>

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Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月8日

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  • Md Abdullah All Mahmud,
  • Sadia Afrin,
  • M. F. Mridha,
  • Sultan Alfarhood,
  • Dunren Che,
  • Mejdl Safran

摘要

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.