人体免疫系统中的白细胞(WBC)可以抵御感染并保护身体免受外部危险物体的侵害。它们由中性粒细胞组成,嗜酸性粒细胞,嗜碱性粒细胞,单核细胞,和淋巴细胞,其中每个占不同的百分比,并执行特定的功能。传统上,用于量化特定类型的白细胞的临床实验室程序是全血细胞计数(CBC)测试的组成部分,这有助于监测人们的健康。随着深度学习的进步,使用各种算法,可以在更少的时间和高精度的血液胶片图像进行分类。本文基于CNN架构开发了许多最先进的深度学习模型及其变体。基于精度的模型性能比较研究,F1分数,召回,精度,参数数量,时间进行了,和DenseNet161被发现在其同行中表现出优异的性能。此外,先进的优化技术,如归一化,混合增强,DenseNet上还采用了标签平滑技术,以进一步完善其性能。
White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance.