[HTML][HTML] Image analysis and machine learning for detecting malaria
Malaria remains a major burden on global health, with roughly 200 million cases worldwide
and more than 400,000 deaths per year. Besides biomedical research and political efforts …
and more than 400,000 deaths per year. Besides biomedical research and political efforts …
[HTML][HTML] Computer vision for microscopy diagnosis of malaria
This paper reviews computer vision and image analysis studies aiming at automated
diagnosis or screening of malaria infection in microscope images of thin blood film smears …
diagnosis or screening of malaria infection in microscope images of thin blood film smears …
[HTML][HTML] Deep learning based automatic malaria parasite detection from blood smear and its smartphone based application
Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is
detected by trained microscopists who analyze microscopic blood smear images. Modern …
detected by trained microscopists who analyze microscopic blood smear images. Modern …
Automatic detection and classification of leukocytes using convolutional neural networks
J Zhao, M Zhang, Z Zhou, J Chu, F Cao - Medical & biological engineering …, 2017 - Springer
The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a
hot issue because of its important applications in disease diagnosis. Nowadays the …
hot issue because of its important applications in disease diagnosis. Nowadays the …
Detection of subtype blood cells using deep learning
Deep Learning has already shown power in many application fields, and is accepted by
more and more people as a better approach than the traditional machine learning models. In …
more and more people as a better approach than the traditional machine learning models. In …
Analysis of infected blood cell images using morphological operators
This work describes a system for detecting and classifying malaria parasites in images of
Giemsa stained blood slides in order to evaluate the parasitaemia of the blood. The first aim …
Giemsa stained blood slides in order to evaluate the parasitaemia of the blood. The first aim …
A neural‐network‐based approach to white blood cell classification
This paper presents a new white blood cell classification system for the recognition of five
types of white blood cells. We propose a new segmentation algorithm for the segmentation …
types of white blood cells. We propose a new segmentation algorithm for the segmentation …
Segmentation of white blood cell from acute lymphoblastic leukemia images using dual‐threshold method
Y Li, R Zhu, L Mi, Y Cao, D Yao - … and mathematical methods in …, 2016 - Wiley Online Library
We propose a dual‐threshold method based on a strategic combination of RGB and HSV
color space for white blood cell (WBC) segmentation. The proposed method consists of …
color space for white blood cell (WBC) segmentation. The proposed method consists of …
Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques
This paper presents a novel, fast and semi-automatic method for accurate cell cluster
segmentation and cell counting of digital tissue image samples. In pathological conditions …
segmentation and cell counting of digital tissue image samples. In pathological conditions …
Leukocytes image classification using optimized convolutional neural networks
M Hosseini, D Bani-Hani, SS Lam - Expert Systems with Applications, 2022 - Elsevier
Hematologic diseases and blood disorders can be studied through the microscopic or
chemical examination of blood smear images. Many researchers work on identifying …
chemical examination of blood smear images. Many researchers work on identifying …