Digital Imaging for Blood Diseases

Digital Imaging for Blood Diseases is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 16k research works in it since 1950. 10.2% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the automated analysis of blood cell images, particularly in the context of detecting malaria parasites and classifying leukemia. The research utilizes techniques such as image processing, convolutional neural networks, and machine learning for tasks including white blood cell segmentation, feature extraction, and automated diagnosis from microscopic blood images.

• Malaria Parasite Detection
• Leukemia Classification
• Image Processing
• Convolutional Neural Networks
• White Blood Cell Segmentation
• Automated Diagnosis
• Microscopic Blood Images
• Machine Learning
• Feature Extraction
• Medical Image Analysis