Explainable AI-Based Skin Cancer Detection Using Deep Learning

Abstract

Skin cancer is one of the most common and clinically significant diseases of the skin, and early detection is important because suspicious lesions can progress rapidly when they are not diagnosed in time. Traditional diagnosis depends on visual inspection, dermoscopy, biopsy, and expert dermatologist judgment, which may be time-consuming and unavailable in many regions. Deep learning methods, especially convolutional neural networks, have improved automated lesion classification from dermoscopic images, but many high-performing models remain difficult to interpret. This survey reviews recent approaches for skin cancer detection using CNNs, transfer learning, multimodal learning, Swish activation, and explainable artificial intelligence techniques such as Grad-CAM, LIME, and SHAP. It compares existing systems, identifies limitations related to black-box decision-making, dataset bias, generalization, computational cost, and clinical trust, and presents a proposed framework for explainable skin lesion classification using a Swish-activated CNN trained on dermoscopic images. The study concludes that accurate and interpretable AI can support dermatologists and telemedicine platforms when model explanations, careful validation, and ethical deployment are treated as core design requirements.

Introduction

Skin cancer occurs due to uncontrolled growth of abnormal skin cells, with melanoma being particularly dangerous because of its high potential to spread. Early detection is crucial, but manual diagnosis using dermoscopy can be slow and dependent on specialist availability, especially in rural or busy clinical settings. This has led to the development of computer-aided diagnosis systems using convolutional neural networks (CNNs) to automatically classify dermoscopic images.

However, traditional CNN models often behave like “black boxes,” providing predictions without explaining how decisions are made, which limits trust in medical use. To address this, explainable AI (XAI) techniques such as Grad-CAM, LIME, and SHAP are used to highlight which image regions or features influenced a model’s decision. Swish activation functions further improve CNN performance by enhancing feature learning.

The literature shows that while deep learning models achieve high accuracy in skin cancer detection, challenges remain in explainability, computational cost, and generalization. The proposed system combines a Swish-activated CNN with explainable AI methods to improve both classification accuracy and interpretability, supporting more reliable clinical decision-making.

Conclusion

This survey presented a structured review of explainable AI-based skin cancer detection using deep learning. The study shows that CNN models can improve automated dermoscopic-image classification, but black-box prediction remains a serious limitation for healthcare applications. Explainable AI techniques such as Grad-CAM, LIME, and SHAP can make predictions more transparent by showing the image regions and features that influence the final result. The proposed framework combines preprocessing, augmentation, Swish-activated CNN feature learning, performance evaluation, and explanation generation. This design can support early screening and clinical decision support when it is validated carefully. Future work should focus on external testing, class imbalance handling, fairness across diverse patient groups, lightweight deployment, and expert evaluation of explanation quality.

References

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Copyright

Copyright © 2026 Prof. Vyshnavi M, K A Ganesh, Dheeraj V M , Channabasava V M , Jashwanth V . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.