Scholarlink

<p>Malaria remains a significant global health challenge, particularly in sub-Saharan Africa. Despite advancements in treatment and prevention, malaria continues to cause substantial morbidity and mortality, particularly among vulnerable populations such as children and pregnant women. Although effective, traditional diagnostic methods, such as microscopy, are time-consuming and require skilled personnel prone to human error, leading to delays in diagnosis and treatment. More so, existing machine learning models used in malaria detection and classification have low performance and overfitting issues. This study presents an enhanced malaria detection and classification model using an ensemble of Convolutional Neural Networks (CNN) and Vision Transformers (ViT). The proposed ensemble model, which combines CNN and ViT, outperforms each individual model, achieving an accuracy of 99.64%, precision of 99.23%, recall of 99.75%, F1 score of 99.51%, and a cross-entropy loss of 0.01. The proposed model demonstrated superior performance compared to those reported in the literature. These results highlight the potential of the CNN-ViT ensemble model for accurate and reliable malaria detection, offering a significant improvement over existing methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing malaria detection and classification using convolutional neural networks-vision transformer architecture

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月6日

查看全文
  • Emmanuel Ahishakiye,
  • Fredrick Kanobe,
  • Danison Taremwa,
  • Bartha Alexandra Nantongo,
  • Leonard Nkalubo,
  • Shallon Ahimbisibwe

摘要

Malaria remains a significant global health challenge, particularly in sub-Saharan Africa. Despite advancements in treatment and prevention, malaria continues to cause substantial morbidity and mortality, particularly among vulnerable populations such as children and pregnant women. Although effective, traditional diagnostic methods, such as microscopy, are time-consuming and require skilled personnel prone to human error, leading to delays in diagnosis and treatment. More so, existing machine learning models used in malaria detection and classification have low performance and overfitting issues. This study presents an enhanced malaria detection and classification model using an ensemble of Convolutional Neural Networks (CNN) and Vision Transformers (ViT). The proposed ensemble model, which combines CNN and ViT, outperforms each individual model, achieving an accuracy of 99.64%, precision of 99.23%, recall of 99.75%, F1 score of 99.51%, and a cross-entropy loss of 0.01. The proposed model demonstrated superior performance compared to those reported in the literature. These results highlight the potential of the CNN-ViT ensemble model for accurate and reliable malaria detection, offering a significant improvement over existing methods.