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A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset
Tissue & Cell ( IF 2.6 ) Pub Date : 2020-12-31 , DOI: 10.1016/j.tice.2020.101473
Aimon Rahman 1 , Hasib Zunair 2 , Tamanna Rahman Reme 1 , M Sohel Rahman 3 , M R C Mahdy 1
Affiliation  

Malaria, one of the leading causes of death in underdeveloped countries, is primarily diagnosed using microscopy. Computer-aided diagnosis of malaria is a challenging task owing to the fine-grained variability in the appearance of some uninfected and infected class. In this paper, we transform a malaria parasite object detection dataset into a classification dataset, making it the largest malaria classification dataset (63,645 cells), and evaluate the performance of several state-of-the-art deep neural network architectures pretrained on both natural and medical images on this new dataset. We provide detailed insights into the variation of the dataset and qualitative analysis of the results produced by the best models. We also evaluate the models using an independent test set to demonstrate the model's ability to generalize in different domains. Finally, we demonstrate the effect of conditional image synthesis on malaria parasite detection. We provide detailed insights into the influence of synthetic images for the class imbalance problem in the malaria diagnosis context.



中文翻译:

高变异疟原虫分类数据集上深度学习架构的比较分析

疟疾是不发达国家的主要死亡原因之一,主要通过显微镜诊断。由于某些未感染和已感染类别的外观存在细粒度可变性,因此计算机辅助疟疾诊断是一项具有挑战性的任务。在本文中,我们将疟疾寄生虫对象检测数据集转换为分类数据集,使其成为最大的疟疾分类数据集(63,645 个细胞),并评估了几种最先进的深度神经网络架构的性能和这个新数据集上的医学图像。我们提供对数据集变化的详细见解,并对最佳模型产生的结果进行定性分析。我们还使用独立的测试集来评估模型以演示模型' 在不同领域进行概括的能力。最后,我们证明了条件图像合成对疟疾寄生虫检测的影响。我们提供了合成图像对疟疾诊断环境中类别不平衡问题的影响的详细见解。

更新日期:2021-01-18
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