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<p>Measles is a highly contagious viral illness with potentially serious health implications, making early and accurate diagnosis essential. The paper improves automated classification and early detection of the disease by introducing a hybrid deep learning framework. The proposed framework makes use of convolutional neural network (CNN) to extract spatial features in the detection of measles through the analysis of lesion image dataset. It is followed by classification of features using dual classifiers namely random forest (RF) and K-nearest neighbour (KNN) for achieving results. The proposed model (CNN + RF + KNN) attained the highest accuracy of 99% which show that integration of transfer learning with traditional classifiers can significantly boost the precision and effectiveness of lesion image detection.</p>

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CNN-RF-KNN: hybrid deep learning framework for detection of measles

  • 期刊论文

  • 发表日期：

    2025年6月29日

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  • Bhukya Ramesh Naik,
  • K. Vinutha,
  • Showkat A. Dar,
  • G. S. Nijaguna,
  • Zameer Ahmed Adhoni,
  • K. R. Swetha

摘要

Measles is a highly contagious viral illness with potentially serious health implications, making early and accurate diagnosis essential. The paper improves automated classification and early detection of the disease by introducing a hybrid deep learning framework. The proposed framework makes use of convolutional neural network (CNN) to extract spatial features in the detection of measles through the analysis of lesion image dataset. It is followed by classification of features using dual classifiers namely random forest (RF) and K-nearest neighbour (KNN) for achieving results. The proposed model (CNN + RF + KNN) attained the highest accuracy of 99% which show that integration of transfer learning with traditional classifiers can significantly boost the precision and effectiveness of lesion image detection.