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Improving CADx system performance for skin disease detection using ensemble machine learning models
Asaduzzaman, Abu Thompson, Christian C. Uddin, Md J.
Asaduzzaman, Abu
Thompson, Christian C.
Uddin, Md J.
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2024-05-02
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Article
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Keywords
Machine learning,Skin diseases,Signal processing and analysis,Computer-aided diagnosis,Convolutional neural network,Support vector machine
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Citation
Abu Asaduzzaman, Christian C. Thompson, Md J. Uddin. Improving CADx System Performance for Skin Disease Detection using Ensemble Machine Learning Models. TechRxiv. May 02, 2024. DOI: 10.36227/techrxiv.171467652.28972099/v1
Abstract
Conventional computer-aided diagnosis (CADx) systems play a crucial role in assisting medical professionals with the detection of skin diseases. However, these systems often involve manual, time-consuming, and error-prone processes. Recent studies show that machine learning models have potential to improve the accuracy of CADx systems. In this work, we present research findings aimed at improving the performance of CADx systems for detecting skin diseases by applying 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); and an ensemble model of CNN with SVM. The HAM10000 dataset from Kaggle is used to train and test all classification models. Resampling is employed to address class imbalance in the dataset. Through rigorous experiments, the results highlight the compelling efficacy of the ensemble CNNSVM model, unveiling heightened accuracy up to 92% (from CNN accuracy 85% and SVM accuracy 83%). The outcome of this work has profound implications for artificial intelligence (AI) accelerated medical domains in advancing the accuracy and efficiency of skin disease treatment.
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e-Prints posted on TechRxiv are preliminary reports that are not peer reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in the media as established information.
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TechRxiv