Feature Selection and Recognition Based on SAR Image

Wan, Zhenzhen; Jiang, Chaoshu; Liu, Yanfang; Lin, Jie; Xu, Wei; Qu, Xiaojie; Min, Xiangtao; Zhang, Xiaoyu

doi:10.1109/radar53847.2021.10028339

Cited by 1 publication

(1 citation statement)

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“…Feature extraction involves the technique of detecting and extracting the most critical and relevant characteristics from segmented regions [28], [29], [30]. Feature selection involves the process of selecting the most relevant and informative characteristics from a vast set of features derived from medical images [31], [32], [33]. The selected features or regions of interest are used as input in the classification step.…”

Section: A Cadx Systemsmentioning

confidence: 99%

Improving CADx System Performance for Skin Disease Detection using Ensemble Machine Learning Models

Asaduzzaman,

Thompson,

Uddin

2024

Preprint

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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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