Cargando…
Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models
BACKGROUND: Manual microscopic examination of Leishman/Giemsa stained thin and thick blood smear is still the “gold standard” for malaria diagnosis. One of the drawbacks of this method is that its accuracy, consistency, and diagnosis speed depend on microscopists’ diagnostic and technical skills. It...
Autores principales: | Abdurahman, Fetulhak, Fante, Kinde Anlay, Aliy, Mohammed |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7938584/ https://www.ncbi.nlm.nih.gov/pubmed/33685401 http://dx.doi.org/10.1186/s12859-021-04036-4 |
Ejemplares similares
-
Tile-based microscopic image processing for malaria screening using a deep learning approach
por: Shewajo, Fetulhak Abdurahman, et al.
Publicado: (2023) -
Modified U-Net for liver cancer segmentation from computed tomography images with a new class balancing method
por: Ayalew, Yodit Abebe, et al.
Publicado: (2021) -
Single-cell conventional pap smear image classification using pre-trained deep neural network architectures
por: Mohammed, Mohammed Aliy, et al.
Publicado: (2021) -
Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs
por: Nepal, Upesh, et al.
Publicado: (2022) -
YOLOv5s-DSD: An Improved Aerial Image Detection Algorithm Based on YOLOv5s
por: Sun, Chaoyue, et al.
Publicado: (2023)