ANOVA测试结果数据集
基于深度学习与OcOA优化的骨髓细胞分类ANOVA测试结果数据集。
基本信息
资源简介
ANOVA测试结果数据集
【数据集背景】
本数据集源站标题为《ANOVA test result.》。源站首次发布：2025-08-29。
【数据内容】
源站原始描述：Manual diagnosis of hematological cancers like leukemia through bone marrow smear analysis is labor-intensive, prone to errors, and highly dependent on expert knowledge. To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. The contributions include developing a baseline Convolutional Neural Ne…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 10.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0330228.t010。
【主题与分类】
源站学科分类：Space Science、Biotechnology、Sociology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified。主题标签：false negative rate、effective feature selection、driven deep learning、deep learning models、contributions include developing、clinical diagnostics quantifiably、average classification error、automated cytology technology、optimization also results、metaheuristic optimization techniques。
【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/ANOVA_test_result_/30014903
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Khafaga, Doaa Sami; El-kenawy, El-Sayed M.; Rizk, Faris H.; Eid, Marwa M.; Khodadadi, Ehsaneh; Khodadadi, Nima (2025). ANOVA test result.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：ANOVA test result.
- DOI：10.1371/journal.pone.0330228.t010
- 发布日期：2025-08-29
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/ANOVA_test_result_/30014903
命令行下载
curl -L -o "Table 10.xls" "https://ndownloader.figshare.com/files/57516731"
文件清单
Table 10.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Manual diagnosis of hematological cancers like leukemia through bone marrow smear analysis is labor-intensive, prone to errors, and highly dependent on expert knowledge. To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. Additionally, utilize the continuous version of OcOA for hyperparameter optimization, further enhancing CNN performance to a maximum accuracy of 98.24%. Crucially, this optimization also results in a substantial clinical performance gain, with sensitivity increasing from 86.02% to 98.34% (+12.32%), specificity rising from 86.53% to 98.14% (+11.61%), and the false negative rate being significantly reduced, thereby enhancing diagnostic reliability in critical scenarios. These findings highlight the potential of metaheuristic optimization techniques to improve the effectiveness of deep learning models in clinical diagnostics quantifiably. The proposed approach demonstrates measurable gains in automated cytology technology, offering a scalable, interpretable, and accurate solution for hematological screening applications.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0330228.t010（source: https://plos.figshare.com/articles/dataset/ANOVA_test_result_/30014903）。
数据缺失？
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