An Efficient VGG19 Framework for Malaria Detection in Blood Cell Images
Malaria diagnosis by microscopy is a method for identifying malaria using cell pictures. In
order to do this, a blood sample must be examined under a microscope to determine …
order to do this, a blood sample must be examined under a microscope to determine …
[HTML][HTML] Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review
Malaria is a disease that affects millions of people worldwide with a consistent mortality rate.
The light microscope examination is the gold standard for detecting infection by malaria …
The light microscope examination is the gold standard for detecting infection by malaria …
AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images
Malaria is a significant public health concern, with∼ 95% of cases occurring in Africa, but
accurate and timely diagnosis is problematic in remote and low-income areas. Here, we …
accurate and timely diagnosis is problematic in remote and low-income areas. Here, we …
An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Background Malaria is a serious public health concern worldwide. Early and accurate
diagnosis is essential for controlling the disease's spread and avoiding severe health …
diagnosis is essential for controlling the disease's spread and avoiding severe health …
Microscopic parasite malaria classification using best feature selection based on generalized normal distribution optimization
Malaria disease can indeed be fatal if not identified and treated promptly. Due to
advancements in the malaria diagnostic process, microscopy techniques are employed for …
advancements in the malaria diagnostic process, microscopy techniques are employed for …
Digital Medical Images Segmentation by Active Contour Model based on the Signed Pressure Force Function
The signed pressure force (SPF) function has recently become a popular function for guiding
the curve evolution of the active contour model (ACM) for image segmentation. The aim is to …
the curve evolution of the active contour model (ACM) for image segmentation. The aim is to …
[PDF][PDF] Malaria cell identification using improved machine learning and modified deep learning architecture
S Shashikiran, HD Sunitha - Indonesian Journal of …, 2024 - pdfs.semanticscholar.org
Malaria continues to be a serious problem for public health because of its occurrence in
tropical and subtropical areas with inadequate healthcare systems and few resources. For …
tropical and subtropical areas with inadequate healthcare systems and few resources. For …
[PDF][PDF] Partitioning intensity inhomogeneity colour images via Saliency-based active contour
Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem
in computer vision and image shape analysis. Given an input image, the active contour …
in computer vision and image shape analysis. Given an input image, the active contour …
A novel two-staged deep learning based workflow for analyzable metaphase detection
HI Turkmen - Multimedia Tools and Applications, 2024 - Springer
In the field of cytogenetics, chromosome image analysis plays a critical role in the diagnosis
of various genetic disorders and cancers. As the gold standard, chromosome image analysis …
of various genetic disorders and cancers. As the gold standard, chromosome image analysis …
Oil Palm Loose Fruit Detection using YOLOv4 for an Autonomous Mobile Robot Collector
AF Japar, HR Ramli, NMH Norsahperi… - IEEE Access, 2024 - ieeexplore.ieee.org
This study researches the usage of YOLOv4 for real-time loose fruit detection in oil palm
plantations as the first step in implementing automation in the collection of loose fruits. Our …
plantations as the first step in implementing automation in the collection of loose fruits. Our …