A Review on Impact of Deep Learning and TensorFlow in Medical Image Processing

Exponential raise in data and images leads to evolution of Artificial Intelligence. Deep learning is a domain of Machine Learning and a unique form of Artificial Neural Networks from AI. These peculiar features facilitated to apply Deep Learning technique on Medical images. These applications acquire deep insights from the image processing. Diagnosing, tumor detection, predictive modeling on electronic health records, classification on clinical heart failures and Blood flow Quantification and visualization are some of the real time applications in health care industry for image processing. Bio-medical images have numerous parameters and variations with respect to dimensions and channels. Hence the images require deep analysis to detect subtle dissimilarities. De-noising, spatial normalization, bias-field correction and nature of acquisition are some of the challenges while handling image datasets. These challenges are addressed by applying TensorFlow framework using deep learning methods. In this paper a review on image processing using deep learning is discussed in order to improve the efficiency of the models by adapting tensor flow framework.