Cross-validation results across five folds.
基于606276张血涂片图像的疟疾诊断深度学习模型交叉验证结果，最高准确率98.1%。
基本信息
资源简介
Cross-validation results across five folds.
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 5.63 KB，主题涵盖 resnet50 obtained 81、resnet18 reached 62、remote healthcare facilities、final augmented dataset、explainability frameworks demonstrate，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
源站首次发布：2025-06-04。
【数据内容】
源站原始描述：Malaria continues to be a severe health problem across the globe, especially within resource-limited areas which lack both skilled diagnostic personnel and diagnostic equipment. This study investigates the use of deep learning diagnosis for malaria through ConvNeXt models that incorporate transfer learning techniques with data augmentation methods for better model performance and transferability. A total number of 606276 thin blood smear images served as the final augmented dataset after the ini…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 7.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0313734.t007。
【主题与分类】
源站学科分类：Microbiology、Cell Biology、Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified。主题标签：resnet50 obtained 81、resnet18 reached 62、remote healthcare facilities、final augmented dataset、explainability frameworks demonstrate、exhibited strong robustness、adamw optimiser produced、version v1 tiny、time diagnostic applications、skilled diagnostic personnel。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Cross-validation_results_across_five_folds_/29240707
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Mmileng, Outlwile Pako; Whata, Albert; Olusanya, Micheal; Mhlongo, Siyabonga (2025). Cross-validation results across five folds.. PLOS ONE. Dataset
下载信息
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公开数据集受托下载与技术交付服务。
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暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Cross-validation results across five folds.
- DOI：10.1371/journal.pone.0313734.t007
- 发布日期：2025-06-04
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Cross-validation_results_across_five_folds_/29240707
命令行下载
curl -L -o "Table 7.xls" "https://ndownloader.figshare.com/files/55124728"
文件清单
Table 7.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Malaria continues to be a severe health problem across the globe, especially within resource-limited areas which lack both skilled diagnostic personnel and diagnostic equipment. This study investigates the use of deep learning diagnosis for malaria through ConvNeXt models that incorporate transfer learning techniques with data augmentation methods for better model performance and transferability. A total number of 606276 thin blood smear images served as the final augmented dataset after the initial 27558 images underwent augmentation. The ConvNeXt Tiny model, version V1 Tiny, achieved an accuracy of 95.9%.; however, the upgraded V2 Tiny Remod version exceeded this benchmark, reaching 98.1% accuracy. The accuracy rate measured 61.4% for Swin Tiny, ResNet18 reached 62.6%, and ResNet50 obtained 81.4%. The combination of label smoothing with the AdamW optimiser produced a model which exhibited strong robustness as well as generalisability. The enhanced ConvNeXt V2 Tiny model combined with data augmentation, transfer learning techniques and explainability frameworks demonstrate a practical solution for malaria diagnosis that achieves high accuracy despite limitations of access to large datasets and microscopy expertise, often observed in resource-limited regions. The findings highlight the potential for real-time diagnostic applications in remote healthcare facilities and the viability of ConvNeXt models in enhancing malaria diagnosis globally.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0313734.t007（source: https://plos.figshare.com/articles/dataset/Cross-validation_results_across_five_folds_/29240707）。
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