Leukemia Detection using Deep Learning

Abstract

This research investigates the application of deep convolutional neural networks (CNNs) for automated detection of leukemia from microscopic blood smear images. The objective is to develop a robust and accurate model capable of differentiating between various types of leukemia and normal blood cells, thereby assisting hematopathologists in diagnosis. The scope encompasses data preprocessing, CNN architecture design, training, validation, and performance evaluation using standard metrics like accuracy, precision, and recall. Results demonstrate improved diagnostic accuracy compared to existing methods, suggesting a valuable tool for enhancing the speed and reliability of leukemia diagnosis.

Introduction

Leukemia, a type of blood cancer, requires timely and accurate diagnosis for effective treatment. Microscopic examination of blood smears remains the gold standard, but it's a time-consuming and subjective process prone to inter-observer variability. This necessitates the development of automated diagnostic tools. Deep learning, particularly CNNs, has shown remarkable success in image classification tasks. However, applying CNNs to leukemia detection faces challenges such as the subtle visual differences between leukemic and normal cells, the need for large annotated datasets, and ensuring the robustness and generalizability of the model.

Objectives

• Develop a deep learning model for accurate classification of different types of leukemia.
• Achieve a higher diagnostic accuracy compared to existing methods.
• Evaluate the robustness and generalizability of the proposed model.