An Explainable Privacy Preserving Multimodal Ensemble Framework For Skin Lesion Classification

Summary

The proposed work aims to develop and evaluate an explainable, privacy-preserving multimodal ensemble fabric arrangement for accurate skin lesion classification by integrating deep learning features, clinical metadata, and explainable AI techniques to improve diagnostic accuracy, transparency, and reliable clinical decision support for early skin cancer detection.

Abstract

Among dermatological diseases, skin cancer is among the most life-threatening. Early and accurate diagnosis is important for improving a patient's prognosis. Nevertheless, traditional AI-based diagnostic methods face several challenges, including privacy concerns, limited interpretability, and a severe class imbalance in multi-class skin lesion datasets. To overcome these challenges, the proposed paper proposes a privacy-aware, explainable multimodal skin lesion classification model that combines complex deep learning models and an ensemble modeling approach with explainable artificial intelligence methods. Experimental evaluation is conducted using publicly available HAM10000 benchmark data on multi-class skin lesion classification that can be accessed by means of Kaggle Hub, distributed over seven clinically significant lesion classes (akiec, bcc, bkl, df, mel, nv, vasc). To balance the data, a class-balancing technique is used to boost the minority classes. The EfficientNet B4, DenseNet201, and MobileNetv2 are used to extract deep feature representations, afterward combined with salient clinical metadata to create a robust multimodal feature space. These multimodal features are used to train XGBoost, LightGBM, Deep Neural Classifier (DNC) that resulted classification accuracies of 92%, 90% with 94% respectively. A stacked ensemble strategy is applied to combine the outputs of XGBoost, LightGBM, and Deep Neural Classifier (DNC), which leads to an improvement in accuracy of 96%. Model interpretability techniques provide feature-level explanations that increase transparency. The experimental findings proved the practicality of the suggested framework in terms of efficiency with clinically relevant real-life classification of skin lesions.

Introduction

Skin cancer represents a significant global health burden, with increasing incidence rates reported worldwide¹. Artificial radiation is recognized as a major contributing factor to skin cancer, leading to genetic mutations that result in uncontrolled cell proliferation and tumor development in skin cells^1,2_. Skin cancers comprise a group of diseases, including melanoma, squamous cell carcinoma, and basal cell carcinoma (bcc). The causes, clinical presentation, and prognostic factors of these conditions all differ³. Skin diseases have become an obstacle....

Protocol

This study used publicly available, fully anonymized dermoscopic datasets and involved no direct human participation; therefore, ethical committee approval was not required. The Table of Materials contains details of all the materials or tools used in this study. Table 1 includes details of the hardware and software environment, such as processor type, memory, operating system, and software frameworks. Table 2 includes details of the class-wise precision, recall, F1-score, and support for each skin lesion category.

Overall workflow of the proposed multimodal skin lesion classificati....

Results

Four classification methods (XGBoost, LightGBM, a Deep Neural Classifier, and a stacked ensemble model) were evaluated for multi-class skin lesion classification. The models achieved overall accuracies of 92%, 90%, 94%, and 96%, respectively, demonstrating that c

Class-wise performance

A detailed class-wise evaluation, including precision, recall, and F1-score for each lesion category, is provided. For the akiec class (support = 65), the stacked ens.......

Discussion

The current protocol outlines a reproducible pipeline for creating an interpretable, privacy-sensitive, multimodal framework to automatically classify skin lesions. The protocol follows a systematic pattern of enhancing diagnostic performance through model transparency, combining dermoscopic image analysis with clinical metadata and interpretable machine learning methods. The HAM10000 skin lesion dataset is publicly available and allows the standardized assessment and facilitates the reproducibility of further research i.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
DenseNet201 CNN Architecture	IBM	https://arxiv.org/abs/1608.06993	Deep learning model for image classification
EfficientNet-B4 CNN Architecture	Google	https://arxiv.org/abs/1905.11946	Deep learning model for image classification
Google Colaboratory Platform	Google	https://colab.research.google.com	Cloud-based computational environment
HAM10000 Skin Lesion Dataset	Harvard Dataverse	https://doi.org/10.7910/DVN/DBW86T	Dermoscopic image dataset
Keras Deep Learning API	Google	Version 2.x	Neural network API
LIME Explainability Library	LIME Project	Version 0.x	Model interpretability technique
MobileNetV2 CNN Architecture	Google	https://arxiv.org/abs/1801.04381	Deep learning model for image classification
Matplotlib Visualization Library	Matplotlib Development Team	Version 3.x	Used for generating plots and performance visualization
NVIDIA GPU	NVIDIA	RTX Series	Computational hardware for model training
NumPy Numerical Computing Library	NumPy Developers	Version 1.x	Data analysis software
OpenCV Image Processing Library	OpenCV Foundation	Version 4.x	Image processing library
Pandas Data Analysis Library	Pandas Development Team	Version 1.x	Data analysis software
Python Programming Environment	Python Software Foundation	Version 3.9+	Data analysis software
SHAP Explainability Library	SHAP Project	Version 0.x	Model interpretability technique
SMOTE Oversampling Technique	imbalanced-learn Project	Version 0.x	Class balancing technique for handling imbalanced datasets
Scikit-learn Machine Learning Library	scikit-learn Project	Version 1.x	Machine learning library
TensorFlow Deep Learning Framework	Google	Version 2.x	Deep learning framework