Deep Learning Models for Medical Image Analysis: Challenges and Future Directions
Artificial neural network (ANN) introduced in the 1950s, is a machine learning framework inspired by the functioning of human neurons. However, for a long time the ANN remained inadequate in solving real problems, because of the problems of overfitting and vanishing gradient while training a deep architecture, dearth of computation power, and non availability of enough data for training the framework. This concept has lately re emerged, in the form of Deep Learning (DL) which initially developed for computer vision and became immensely popular in several other domains. It gained traction in late 2012, when a DL approach i.e. convolutional neural network won in the ImageNet Classification an acclaimed worldwide computer vision competition. Thereafter, researchers in practically every domain, including medical imaging, started vigorously contributing in the massively progressing field of DL. The success of DL methods can be owed to the availability of data, boosted computation power provided by the existing graphics processing units (GPUs) and ground breaking training algorithms. In this paper, we have overviewed the area of DL in medical imaging, including (1) machine learning and DL basics, (2) cause of power of DL, (3) common DL models, (4) their applications to medical imaging and (5) challenges and future work in this field.
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