Authors :
B.Nagaraju; R.Srija; M.Komala; P.Dharani
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3MqsjWR
DOI :
https://doi.org/10.5281/zenodo.7935352
Abstract :
Medical imaging is important in a variety of
clinical activities, including early detection, monitoring,
an opinion, and therapy evaluation of many medical
diseases. grasp medical image analysis in a computer
vision requires a solid grasp of the principles and
operations of artificial neural networks, as well as deep
literacy. Deep Learning Approach (DLA) in medical
image processing is emerging as a rapidly increasing
research subject. DLA has been widely utilised in
medical imaging to characterise the presence or absence
of a complaint. The vast majority of DLA executions
focus on X-ray pictures, motorised tomography images,
mammography images, and digital histopathology
images. It presents a rigorous assessment of studies
based on DLA for bracketing, discovery, and
segmentation of medical pictures. This review directs the
experimenters' assumptions.
Keywords :
Artificial Neural Networks, Deep Literacy, Deep Learning Approach (DLA), Motorized Tomography, Mammography Images, Digital Histopathology Images.
Medical imaging is important in a variety of
clinical activities, including early detection, monitoring,
an opinion, and therapy evaluation of many medical
diseases. grasp medical image analysis in a computer
vision requires a solid grasp of the principles and
operations of artificial neural networks, as well as deep
literacy. Deep Learning Approach (DLA) in medical
image processing is emerging as a rapidly increasing
research subject. DLA has been widely utilised in
medical imaging to characterise the presence or absence
of a complaint. The vast majority of DLA executions
focus on X-ray pictures, motorised tomography images,
mammography images, and digital histopathology
images. It presents a rigorous assessment of studies
based on DLA for bracketing, discovery, and
segmentation of medical pictures. This review directs the
experimenters' assumptions.
Keywords :
Artificial Neural Networks, Deep Literacy, Deep Learning Approach (DLA), Motorized Tomography, Mammography Images, Digital Histopathology Images.
