1 research outputs found
Medical Image Analysis using Convolutional Neural Networks: A Review
The science of solving clinical problems by analyzing images generated in
clinical practice is known as medical image analysis. The aim is to extract
information in an effective and efficient manner for improved clinical
diagnosis. The recent advances in the field of biomedical engineering has made
medical image analysis one of the top research and development area. One of the
reason for this advancement is the application of machine learning techniques
for the analysis of medical images. Deep learning is successfully used as a
tool for machine learning, where a neural network is capable of automatically
learning features. This is in contrast to those methods where traditionally
hand crafted features are used. The selection and calculation of these features
is a challenging task. Among deep learning techniques, deep convolutional
networks are actively used for the purpose of medical image analysis. This
include application areas such as segmentation, abnormality detection, disease
classification, computer aided diagnosis and retrieval. In this study, a
comprehensive review of the current state-of-the-art in medical image analysis
using deep convolutional networks is presented. The challenges and potential of
these techniques are also highlighted