Open access

Towards Transparent Decisions: CNN Ensemble with XAI-Driven Interpretations

A. D. Hayder J. Saeed

2026 · passer of basic and applied sciences · 0 citations · 25 references

TL;DR

A comprehensive deep learning-based framework that leverages three benchmark datasets—PH2, ISIC (Benign vs Malignant), and HAM10000—using transfer learning and ensemble techniques is presented, demonstrating a notable increase in classification accuracy.

Abstract

Skin cancer remains a global health threat with increasing incidence rates. Accurate and early classification of skin lesions into benign or malignant types is essential for timely treatment and prevention of severe outcomes. In this paper, we present a comprehensive deep learning-based framework that leverages three benchmark datasets—PH2, ISIC (Benign vs Malignant), and HAM10000—using transfer learning and ensemble techniques. Pre-trained models including VGG16, ResNet50, and EfficientNetB4 were fine-tuned on each dataset, and majority voting was employed to combine predictions. The Gradient-weighted Class Activation Mapping (Grad-CAM) was also used to improve visual explainability. The findings demonstrate a notable increase in classification accuracy, surpassing current techniques and reaching over 98% accuracy on certain datasets. This study highlights the impact of hybrid architectures and explainable AI in advancing the state of skin cancer diagnosis systems.

DOI Semantic Scholar

Read PDF

Similar papers

Sep 2026

Optimization-driven feature selection for enhanced skin cancer detection using deep learning

Improved robustness and explainability in lesion classification is demonstrated, supporting the applicability of the proposed method to real clinical settings and future research focuses on multimodal fusion and lightweight deployment on edge devices.

Amruta Thorat, Chaya Jadhav · Cluster Computing · 0 citations

#graph neural networks Open access Sep 2026

STLC-Net: Skin Transfer Learning Classification Framework for Multi-Class Skin Cancer Detection and Clinical Decision Support

STLC-Net (Skin Transfer Learning Classification Network) is introduced, a robust, zero-data-leakage framework for automated seven-class skin lesion classification using the benchmark HAM10000 dataset and integrated into an interactive web deployment for real-time, interpretable clinical decision support.

Gaurav Bhabhar · Interdisciplinary Journal of... · 0 citations

#explainable ai Open access Sep 2026

Explainable ensemble transfer learning for skin lesion classification with multi-method explainability validation

Quantitative evaluation using TMAI and complementary XAI methods provides additional evidence that the model's predictions are grounded in clinically relevant lesion regions rather than background artefacts, supporting the potential of the framework for accurate and interpretable automated skin lesion classification.

NE. Sravani, Srinivas Koppu · Frontiers in Public Health · 0 citations

Open access Aug 2026

Explainable Deep Learning for Computer-Aided Skin Cancer Detection Using CNNs and Vision Transformers

Results demonstrate that CNN-based architectures achieve robust performance in capturing local spatial features, while transformer-based models provide competitive results through global contextual representation, however, variations are observed in model calibration and false-negative rates, which are critical for cli...

Eirini Karantina, Antreas Kantaros, Grigoris Nikolaou et al. · Algorithms · 0 citations

Preprint Aug 2026

Uncertainty-Aware and Explainable Ensemble Deep Learning Framework for Multi-Class Skin Lesion Classification

This paper proposes an uncertainty-aware and explainable deep learning framework for multi-class skin lesion classification and demonstrates accurate, interpretable, and uncertainty-aware skin lesion classification for trustworthy computer-aided diagnosis.

Rofiqul Islam, Lilatul Ferdouse · 0 citations

Deep Learning-Based Automated Skin Cancer Detection Using Dermoscopic Images

The results demonstrate that deep learning techniques can significantly assist in early detection and classification of skin cancer, thereby supporting dermatologists in clinical decision-making and improving diagnostic efficiency and mortality rates associated with skin cancer.

A. Star, Gibi Linza, Siva Durshika et al. · 0 citations