FusionEXNet: an interpretable fused deep learning model for skin cancer detection

Abstract

Skin cancer poses a significant burden on mankind and healthcare systems globally, necessitating the development of effective diagnostic and treatment strategies. This paper introduces FusionEXNet, an innovative and interpretable fused deep-learning model for skin cancer detection. FusionEXNet leverages the strengths of EfficientNetV2S and XceptionNet architectures to extract robust features from dermoscopic images, achieving superior performance compared to individual models. While XceptionNet and EfficientNetV2S attained accuracies of 88.82% and 88.01%, respectively, FusionEXNet surpassed them with an impressive accuracy of 90.83%. To enhance model interpretability, Explainable Artificial Intelligence (XAI) techniques, such as SmoothGrad and Faster Score-CAM, are integrated, providing valuable insights into the decision-making process. The model was trained and evaluated using the extensive HAM10000 dataset, consisting of over 10,000 high-resolution images across seven skin lesion categories. The proposed FusionEXNet model offers a reliable, accurate, and interpretable system for skin cancer detection, contributing to improved patient outcomes and more efficient diagnostic processes. This study underscores the potential of combining advanced CNN architectures with XAI methods to create powerful tools for dermatological diagnostics.

KEYWORDS:

• Dermatological disease
• XceptionNet, efficientNetV2S
• fusion
• SmoothGrad
• XAI

Disclosure statement

No potential conflict of interest was reported by the author(s).

Compliance with ethical standards

This research did not involve human or animal participation. All authors have checked and agreed on the submission.

Data availability statement

The data that support the findings of this study are available at https://github.com/Piyush-Guptaa/FusionEXNet. The dataset used in the experimentation is available in the public domain at: https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000.