使用新型深度增强与集成学习框架进行疟疾寄生虫检测
Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework
AI总结：
针对疟疾人工筛查费力且易错的问题，提出新型深度增强与集成学习（DBEL）框架，通过堆叠Boosted-BR-STM卷积网络与集成ML分类器提取深层判别特征，在NIH数据集上准确率达98.50%，显著提升检测性能。
摘要：
疟疾是一种可能致命的疟原虫感染疾病，由雌性按蚊传播，每年在全球感染数百万人并侵入红细胞。然而，临床实践中专家的徒手筛查既费力又容易出错。因此，开发了一种新型深度增强与集成学习（DBEL）框架，该框架包含新型 Boosted-BR-STM 卷积神经网络（CNN）的堆叠以及集成 ML 分类器，用于筛查疟疾寄生虫图像。所提出的 Boosted-BR-STM 基于新型基于膨胀卷积块的分割-变换-合并（STM）以及特征图挤压-增强（SB）思想。此外，新的 STM 块利用区域和边界操作来学习疟疾寄生虫的同质性、异质性及边界模式。另外，通过在 STM 块的抽象层、中间层和结论层采用基于迁移学习的新型特征图 SB，获得了多样的增强通道，以学习寄生虫模式的微小强度和纹理变化。所提出的 DBEL 框架堆叠了显著且多样的增强通道，并将所开发的 Boosted-BR-STM 生成的判别性特征提供给 ML 分类器集成。该框架提高了集成学习的判别能力和泛化能力。此外，将所开发的 Boosted-BR-STM 和定制 CNN 的深度特征空间输入到 ML 分类器中进行比较分析。所提出的 DBEL 框架在使用离散小波变换增强以丰富特征空间的 NIH 疟疾数据集上优于现有技术。该框架实现了准确率（98.50%）、灵敏度（0.9920）、F分数（0.9850）和 AUC（0.997），表明其可用于疟疾寄生虫筛查。
英文摘要：
Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and millions worldwide yearly. However, specialists' manual screening in clinical practice is laborious and prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL) framework, comprising the stacking of new Boosted-BR-STM convolutional neural networks (CNN) and the ensemble ML classifiers, is developed to screen malaria parasite images. The proposed Boosted-BR-STM is based on a new dilated-convolutional block-based split transform merge (STM) and feature-map Squeezing-Boosting (SB) ideas. Moreover, the new STM block uses regional and boundary operations to learn the malaria parasite's homogeneity, heterogeneity, and boundary with patterns. Furthermore, the diverse boosted channels are attained by employing Transfer Learning-based new feature-map SB in STM blocks at the abstract, medium, and conclusion levels to learn minute intensity and texture variation of the parasitic pattern. The proposed DBEL framework implicates the stacking of prominent and diverse boosted channels and provides the generated discriminative features of the developed Boosted-BR-STM to the ensemble of ML classifiers. The proposed framework improves the discrimination ability and generalization of ensemble learning. Moreover, the deep feature spaces of the developed Boosted-BR-STM and customized CNNs are fed into ML classifiers for comparative analysis. The proposed DBEL framework outperforms the existing techniques on the NIH malaria dataset that are enhanced using discrete wavelet transform to enrich feature space. The proposed DBEL framework achieved Accuracy (98.50%), Sensitivity (0.9920), F-score (0.9850), and AUC (0.997), which suggest it to be utilized for malaria parasite screening.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
