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Application of ensemble learning models in computer-aided diagnosis of skin diseases.

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Title: Application of ensemble learning models in computer-aided diagnosis of skin diseases.
Authors: Asaduzzaman, Abu1 (AUTHOR) abu.asaduzzaman@wichita.edu, Thompson, Christian C.1 (AUTHOR), Sibai, Fadi N.2 (AUTHOR), Uddin, Md J.3 (AUTHOR)
Source: Neural Computing & Applications. Jul2025, Vol. 37 Issue 21, p16735-16751. 17p.
Subjects: Computer-aided diagnosis, Skin diseases, Artificial intelligence, Ensemble learning, Machine learning, Classification algorithms
Abstract: Computer-aided diagnosis (CADx) systems often involve error-prone and/or manual processes. Recent studies show that various machine learning models have the potential to improve the accuracy of CADx systems. However, existing models suffer from low prediction accuracy. In this work, we present research findings to improve the effectiveness of CADx systems for detecting skin diseases by adopting optimized ensemble machine learning models. The investigation encompasses the exploration of three popular classification methods: linear discriminant analysis (LDA), support vector machine (SVM), and convolutional neural network (CNN); two customized CNN models: LeNet-5 and ResNet; and an ensemble model of CNN with SVM. The ensemble CNN-SVM model is optimized using techniques such as feature aggregation and weight adjustments. Skin lesion images from Kaggle's Human Against Machine 10000 (HAM10000) are used to train and test all classification models. Through rigorous experiments, the results highlight the compelling efficacy of the ensemble CNN-SVM model, unveiling heightened accuracy of up to 92% (from ResNet accuracy of 88%, CNN accuracy of 85%, SVM accuracy of 83%, LeNet-5 accuracy of 77%, and LDA accuracy of 75%). The models are tested on another dataset from Kaggle's Melanoma Skin Cancer Dataset of 10000 Images; new results follow a similar trend to those using the HAM10000 dataset. The outcome of this work has profound implications for artificial intelligence (AI) accelerated engineering applications in advancing the effectiveness of skin disease treatment through diagnosis systems. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Computer-aided+diagnosis%22">Computer-aided diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Skin+diseases%22">Skin diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink>
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  Data: Computer-aided diagnosis (CADx) systems often involve error-prone and/or manual processes. Recent studies show that various machine learning models have the potential to improve the accuracy of CADx systems. However, existing models suffer from low prediction accuracy. In this work, we present research findings to improve the effectiveness of CADx systems for detecting skin diseases by adopting optimized ensemble machine learning models. The investigation encompasses the exploration of three popular classification methods: linear discriminant analysis (LDA), support vector machine (SVM), and convolutional neural network (CNN); two customized CNN models: LeNet-5 and ResNet; and an ensemble model of CNN with SVM. The ensemble CNN-SVM model is optimized using techniques such as feature aggregation and weight adjustments. Skin lesion images from Kaggle's Human Against Machine 10000 (HAM10000) are used to train and test all classification models. Through rigorous experiments, the results highlight the compelling efficacy of the ensemble CNN-SVM model, unveiling heightened accuracy of up to 92% (from ResNet accuracy of 88%, CNN accuracy of 85%, SVM accuracy of 83%, LeNet-5 accuracy of 77%, and LDA accuracy of 75%). The models are tested on another dataset from Kaggle's Melanoma Skin Cancer Dataset of 10000 Images; new results follow a similar trend to those using the HAM10000 dataset. The outcome of this work has profound implications for artificial intelligence (AI) accelerated engineering applications in advancing the effectiveness of skin disease treatment through diagnosis systems. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Type: general
      – SubjectFull: Skin diseases
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: Ensemble learning
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              Text: Jul2025
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              Y: 2025
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