[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 …
Computational methods for automated analysis of malaria parasite using blood smear images: recent advances
Malaria comes under one of the dangerous diseases in many countries. It is the primary
reason for most of the causalities across the world. It is presently rated as a significant cause …
reason for most of the causalities across the world. It is presently rated as a significant cause …
Malaria parasite detection from peripheral blood smear images using deep belief networks
D Bibin, MS Nair, P Punitha - IEEE Access, 2017 - ieeexplore.ieee.org
In this paper, we propose a novel method to identify the presence of malaria parasites in
human peripheral blood smear images using a deep belief network (DBN). This paper …
human peripheral blood smear images using a deep belief network (DBN). This paper …
Machine learning approach for automated screening of malaria parasite using light microscopic images
DK Das, M Ghosh, M Pal, AK Maiti, C Chakraborty - Micron, 2013 - Elsevier
The aim of this paper is to address the development of computer assisted malaria parasite
characterization and classification using machine learning approach based on light …
characterization and classification using machine learning approach based on light …
Classification of malaria using object detection models
Malaria poses a global health problem every day, as it affects millions of lives all over the
world. A traditional diagnosis requires the manual inspection of blood smears from the …
world. A traditional diagnosis requires the manual inspection of blood smears from the …
Clustering-based dual deep learning architecture for detecting red blood cells in malaria diagnostic smears
Computer-assisted algorithms have become a mainstay of biomedical applications to
improve accuracy and reproducibility of repetitive tasks like manual segmentation and …
improve accuracy and reproducibility of repetitive tasks like manual segmentation and …
Malaria detection using deep learning
G Shekar, S Revathy, EK Goud - 2020 4th international …, 2020 - ieeexplore.ieee.org
Malaria is the deadliest disease in the earth and big hectic work for the health department.
The traditional way of diagnosing malaria is by schematic examining blood smears of …
The traditional way of diagnosing malaria is by schematic examining blood smears of …
A review of automated methods for the detection of sickle cell disease
Detection of sickle cell disease is a crucial job in medical image analysis. It emphasizes
elaborate analysis of proper disease diagnosis after accurate detection followed by a …
elaborate analysis of proper disease diagnosis after accurate detection followed by a …
Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears
Malaria parasitemia is the quantitative measurement of the parasites in the blood to grade
the degree of infection. Light microscopy is the most well-known method used to examine …
the degree of infection. Light microscopy is the most well-known method used to examine …
Segmentation based approach for detection of malaria parasites using moving k-means clustering
Recent progress based on microscopic imaging has given significant contribution in
diagnosis of malaria infection based on blood images. Due to the requirement of prompt and …
diagnosis of malaria infection based on blood images. Due to the requirement of prompt and …