AI Powered Leukocyte Classification, A Machine Learning Approach To Blood Diagnostics

Abstract

Diagnosis of various diseases including infections, immune disorders, and leukemia requires white blood cell (WBC) classification. The traditional methods take long hours for WBC classification through manual examination by the hematologists, which may lead to errors by human judgment, as it is difficult and requires much expertise. But with recent developments in deep learning, automated WBC classification is gaining interest due to its accuracy and speed. This study investigates a deep learning approach to WBC classification using the VGG16 convolutional neural network (CNN) architecture.

Transfer learning is used in our model in which a pre-trained VGG16 network has been fine-tuned using a dataset containing labeled WBC images. The dataset contains images of neutrophils, lymphocytes, monocytes, and eosinophils for a truly comprehensive classification task. The model's WBC type differentiation ability is evaluated based on the accuracy, precision, re-call, and F1-score for the different classes. The model generalization is further enhanced using data augmentation, thus preventing overfitting.

The experimental results have shown that the VGG16-based model achieves a very high classification accuracy when compared with other traditional machine learning-based approaches and even some custom deep learning architectures. It demonstrates that deep learning, and especially VGG16, could serve as an important tool to automate WBC classification and thereby cut down on the time for diagnostic purposes while assisting hematological analyses by medical professionals. Future work may involve the integration of the model into real-time diagnostic systems for rare WBC abnormalities in the dataset and the evaluation of more sophisticated architectures for maximized performance. The study showcases how deep learning can change the paradigm of medical image analysis and take diagnostics to another level of accuracy.