What question did this study set out to answer?

The aim is to enhance the classification of multi-class skin disorders using deep learning techniques and generative AI.

April 28, 2026Discover Artificial Intelligence0 citationsOpen Access

An ensemble deep learning framework for skin disease classification with explainable and generative AI support

FFFaisal FirdousJaypee University of Information Technology DYDhruv YadavJaypee University of Information Technology SRSyed Zoofa RufaiShree Guru Gobind Singh Tricentenary University

Key Points

The aim is to enhance the classification of multi-class skin disorders using deep learning techniques and generative AI.
Utilized pre-trained convolutional neural networks including EfficientNetB0, MobileNetV2, DenseNet121, and ResNet50.
Applied a maximum confidence ensemble technique to combine individual model performances.
Analyzed dermoscopic images from the HAM10000 and ISIC2019 datasets focusing on five skin disorder classes.
Achieved individual model training accuracy over 96% with test accuracy ranging from 85% to 88%.
Proposed ensemble model reached a test accuracy of 90.74%, indicating significant performance improvement.
Demonstrated generative AI's capability in providing detailed disease-related information alongside predictions.

Abstract

Accurate and timely identification of skin disorders from dermoscopic images is essential to support effective clinical decisions. In this paper, a deep learning-based approach is proposed to classify multi-class skin disorders using pre-trained convolutional neural networks. Four popular models, including EfficientNetB0, MobileNetV2, DenseNet121, and ResNet50, are trained and compared to evaluate their performance differences. The total number of classes considered for this study was five and one as no class, and the images were taken from the HAM10000 and ISIC2019 datasets. In addition, to improve the robustness of the model performance, a maximum confidence ensemble technique is used to combine the performance results of each individual model. Experimental results indicate that individual models can reach high training accuracy with values over 96%, but their test accuracy values are distributed between 85 and 88%. However, the proposed test accuracy reached 90.74%, showing a significant improvement to reduce model-specific deviation. Apart from the classification, the model also encompasses a generative AI module for the automated generation of structured and disease-specific information regarding the symptoms, causes, and treatment for the predicted class. The model will therefore be discriminative and generative to provide predictions and information to the user. The model has shown the efficiency of the use of ensemble deep learning and generative AI in designing an intelligent health assistance system for the analysis of skin diseases.

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