Authors :
E. Emerson Nithiyaraj; S. Arivazhagan
Volume/Issue :
Volume 5 - 2020, Issue 7 - July
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/2OHSEBb
DOI :
10.38124/IJISRT20JUL058
Abstract :
Computed tomography (CT) scanning is a
non-invasive diagnostic imaging technique that provides
more detailed information about the liver than standard
X-rays. Unlike ultrasound (US) examination, the quality
of the CT image is not highly operator dependent. Plenty
of works has been done using computer-aided diagnosis
(CAD) for liver using conventional machine learning
algorithms with better results. Recent advances especially
in deep learning technology, can detect, classify, segment
patterns in medical images where the advancements in
deep learning has been shifted to medical domain also.
One of the core abilities of deep learning is that they
could learn feature representations automatically from
data instead of feeding hand crafted features based on
application. In this review, the basics of deep learning is
introduced and their success in liver segmentation and
lesion detection, classification using CT imaging modality
is reviewed and their different network architectures is
also discussed. Transfer learning is an interesting
approach in deep learning which is also discussed. So,
deep learning and CAD system has made a huge impact
and has produced enhanced performance in healthcare
industry.
Keywords :
Computed Tomography Scan, Computer-aided diagnosis, Deep learning, Artificial Intelligence.
Computed tomography (CT) scanning is a
non-invasive diagnostic imaging technique that provides
more detailed information about the liver than standard
X-rays. Unlike ultrasound (US) examination, the quality
of the CT image is not highly operator dependent. Plenty
of works has been done using computer-aided diagnosis
(CAD) for liver using conventional machine learning
algorithms with better results. Recent advances especially
in deep learning technology, can detect, classify, segment
patterns in medical images where the advancements in
deep learning has been shifted to medical domain also.
One of the core abilities of deep learning is that they
could learn feature representations automatically from
data instead of feeding hand crafted features based on
application. In this review, the basics of deep learning is
introduced and their success in liver segmentation and
lesion detection, classification using CT imaging modality
is reviewed and their different network architectures is
also discussed. Transfer learning is an interesting
approach in deep learning which is also discussed. So,
deep learning and CAD system has made a huge impact
and has produced enhanced performance in healthcare
industry.
Keywords :
Computed Tomography Scan, Computer-aided diagnosis, Deep learning, Artificial Intelligence.
