Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network.
Khaled AlmezhghwiSertan SertePublished in: Computational intelligence and neuroscience (2020)
White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils, eosinophils, lymphocytes, monocytes, and basophils, where each type constitutes a different proportion and performs specific functions. Being able to classify and, therefore, count these different constituents is critical for assessing the health of patients and infection risks. Generally, laboratory experiments are used for determining the type of a white blood cell. The staining process and manual evaluation of acquired images under the microscope are tedious and subject to human errors. Moreover, a major challenge is the unavailability of training data that cover the morphological variations of white blood cells so that trained classifiers can generalize well. As such, this paper investigates image transformation operations and generative adversarial networks (GAN) for data augmentation and state-of-the-art deep neural networks (i.e., VGG-16, ResNet, and DenseNet) for the classification of white blood cells into the five types. Furthermore, we explore initializing the DNNs' weights randomly or using weights pretrained on the CIFAR-100 dataset. In contrast to other works that require advanced image preprocessing and manual feature extraction before classification, our method works directly with the acquired images. The results of extensive experiments show that the proposed method can successfully classify white blood cells. The best DNN model, DenseNet-169, yields a validation accuracy of 98.8%. Particularly, we find that the proposed approach outperforms other methods that rely on sophisticated image processing and manual feature engineering.
Keyphrases
- deep learning
- induced apoptosis
- cell cycle arrest
- convolutional neural network
- machine learning
- signaling pathway
- stem cells
- chronic kidney disease
- computed tomography
- public health
- end stage renal disease
- patient safety
- bone marrow
- magnetic resonance
- mesenchymal stem cells
- newly diagnosed
- magnetic resonance imaging
- mental health
- electronic health record
- optical coherence tomography
- dendritic cells
- peripheral blood
- health information
- risk assessment
- patient reported
- immune response
- light emitting
- network analysis
- pi k akt
- drug induced