Enhancing Hematological Diagnostics: Deep Learning Models for Human Blood Cell Classification

The classification of human blood cells plays a critical role in medical diagnostics, particularly in identifying and treating various hematological conditions. This work investigates how deep learning methods could be used to improve blood cell categorisation accuracy and efficiency. We used a wide range of models, each with special ability for managing image-based data. Recurrent neural networks (RNNs), VGG16, Inception, capsule networks, and deep belief networks (DBNs) were the models used for this work. Sequential data across picture frames was analysed using RNNs to provide understanding of temporal fluctuations in blood cell imaging. Using both convolutional neural networks known for their success in image identification challenges, VGG16 and Inception were used to leverage their strong feature extracting power. These models are very good at handling the minute elements of blood cell images. Appropriate for the complex and overlapping structures often present in blood cell pictures, capsule networks were incorporated to efficiently capture spatial hierarchies and fine features more than conventional CNNs. Finally, DBNs were used for their mastery in unsupervised learning, thus enabling data-efficient feature extraction and classification. The combination of these models aimed to leverage their collective strengths, addressing the challenges of precision and accuracy in classifying the various types of blood cells. The models were trained and validated on a dataset comprising images of red blood cells, white blood cells, and platelets, each labelled according to specific cell types.