Deep Learning for Peripheral Blood Smear Interpretation

Abstract

The objective of this research is to develop a system for detecting and classifying blood cells from images. Identifying blood cell types—Eosinophil’s, Lymphocytes, Monocytes, and Neutrophils—is crucial for medical diagnosis and can be effectively performed through precise image analysis.

In this study, we extracted key features of these blood cells and employed convolutional neural networks (CNNs) to classify them. Our approach integrates deep learning with CNNs to enhance the efficiency and accuracy of blood cell classification. A CNN model was trained using a publicly available blood cell image dataset, and various neuron- and layer-wise visualization techniques were applied. The results indicate that neural networks can capture colour and texture patterns unique to each blood cell type, resembling human decision-making processes.

Additionally, we implemented this system using the Django web framework for deployment, enabling practical application in medical diagnostics. To ensure robust performance, we experimented with different blood cell samples as input to the CNN model, evaluating its classification accuracy and efficiency.