论文标题
使用新的深层增强和合奏学习框架的疟疾寄生虫检测
Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework
论文作者
论文摘要
疟疾是一种潜在的致命疟原虫，由雌性蚊子注射，每年都会感染红细胞和数百万美元的蚊子。但是，专家在临床实践中的手册筛查是费力的，容易出错。因此，一个新颖的深度增强和集合学习（DBEL）框架包括新的BROOST-BR-STM卷积神经网络（CNN）和集合ML分类器的堆叠，以筛选疟疾寄生虫图像。拟议的Boosted-BR-STM基于新的基于扩张的跨跨区域的分裂变换合并（STM）和特征映射挤压 - 增强式（SB）的想法。此外，新的STM块使用区域和边界操作来学习疟原虫的同质性，异质性和与模式的边界。此外，通过在抽象，媒介和结论水平上使用基于转移学习的新功能映射SB来实现多样化的增强渠道，以学习寄生模式的微小强度和纹理变化。所提出的DBEL框架暗示了突出和多样化的增强通道的堆叠，并为ML分类器的集合提供了开发的Boosted-BR-STM的生成的判别特征。提出的框架提高了集合学习的歧视能力和概括。此外，开发的Boosted-BR-STM和自定义CNN的深度特征空间被馈入ML分类器进行比较分析。所提出的DBEL框架的表现优于NIH疟疾数据集中的现有技术，这些技术使用离散小波变换增强以丰富特征空间。提出的DBEL框架达到了准确性（98.50％），灵敏度（0.9920），F-SCORE（0.9850）和AUC（0.997）（0.997），这建议用于疟疾寄生虫筛查。
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.
