Blood Cancer Cell Segmentation and Classification using Deep Learning

Abstract

This research investigates the application of deep learning, specifically convolutional neural networks (CNNs), for the automated segmentation and classification of blood cancer cells from microscopic images. The objective is to develop a robust and accurate system that can assist hematopathologists in diagnosing blood cancers. The scope includes training and evaluating a CNN model on a diverse dataset of blood smear images. The results demonstrate improved accuracy and efficiency compared to traditional methods, offering a potential tool for faster and more reliable diagnosis. Future work will focus on expanding the dataset and integrating the system into clinical workflows.

Introduction

Accurate and timely diagnosis of blood cancers is crucial for effective treatment. Microscopic examination of blood smears remains a cornerstone of diagnosis, but it is time-consuming, labor-intensive, and susceptible to inter-observer variability. Manual analysis of thousands of cells per sample presents a significant bottleneck. Deep learning offers a promising solution by automating the process of identifying and classifying cancer cells. However, challenges remain in handling the variability in cell morphology, image quality, and the need for large, annotated datasets for training robust models. This research addresses these challenges by developing a high-performance deep learning model for automated blood cancer cell analysis.

Objectives

• Develop a deep learning model capable of accurately segmenting blood cancer cells from microscopic images.
• Train and evaluate the model's performance on a diverse dataset of blood smear images.
• Achieve higher accuracy and efficiency compared to existing manual and automated methods.