Search

Reticulocyte count is a routine blood test that can be an essential source of knowledge for medical doctors to diagnose and assess patients’ health condition. In fact, the automation of this blood test will reduce cost and time, in addition to protecting laboratorians’ lives, especially during pandemics and outbreaks. However, human reticulocyte data scarcity is a main challenge that slows the pace of the test automation. In this paper, a novel method that assesses the feasibility of using animal reticulocyte cells as a solution to compensate for the scarcity of human reticulocyte data is investigated. The integration of animal cells will be implemented by utilizing a data-centric artificial intelligence approach, in addition to employing multiple deep classifiers that utilize transfer learning in different experimental setups in a procedure that mimics the protocol followed in experimental medical labs. Moreover, to evaluate the effectiveness of the proposed method, three evaluation criteria have been proposed, namely, the pretraining boost, the dataset similarity boost, and the dataset size boost measures. All the experiments of this work were conducted on a public human reticulocyte dataset and the best performing model achieved 98.9%, 98.9%, 98.6% average accuracy, average macro precision, and average macro F-score respectively. Moreover, the results showed that using animals medical data holds a promising solution for human medical data scarcity, as utilizing weights that were pretrained on a medium size feline reticulocyte dataset outperformed the model that utilized weights that were pretrained on the large scale ImageNet dataset

Peripheral Blood Smear (PBS) analysis is a vital routine test carried out by hematologists to assess some aspects of humans' health status. PBS analysis is prone to human errors and utilizing computer-based analysis can greatly enhance this process in terms of accuracy and cost. Recent approaches in learning algorithms, such as deep learning, are data hungry, but due to the scarcity of labeled medical images, researchers had to find viable alternative solutions to increase the size of available datasets. Synthetic datasets provide a promising solution to data scarcity, however, the complexity of blood smears' natural structure adds an extra layer of challenge to its synthesizing process. In this work, we propose a methodology that utilizes Locality Sensitive Hashing (LSH) to create a novel balanced dataset of 2500 synthetic blood smears. This dataset, which was automatically annotated during the generation phase, will be made public for research purposes and covers 17 essential categories of blood cells. We proved the effectiveness of the proposed dataset by utilizing it for training a deep neural network, this model got a very high accuracy score of 98.72% when tested with the well known ALL-IDB dataset. The dataset also got the approval of 5 experienced hematologists to meet the general standards of making thin blood smears.