Advances in Deep Learning Techniques for Medical Image Analysis

Deep learning is contributing to the high level of services to the healthcare sector. As the digital medical data is increasing exponentially with time, early detection and prediction of diseases are becoming more efficient because of the deep learning techniques which reduce the fatality rate to a great extent. The main focus of this paper is to provide the comprehensive review of deep learning in the domain of medical image processing and analysis. We have demonstrated the use of new deep learning architectures in oncology for the prediction of different types of cancer like the brain, lung, skin, etc. The state-of-the-art architectures effectively carry out analysis of 2D and 3D medical images to make the diagnosis of patients faster and more accurate. The use of popular approaches in machine learning such as ensemble and transfer learning with fine-tuning of parameters improve the performance of the deep neural networks in medical image analysis. The existing deep networks urge the new image classification network called Capsule Network (CapsNet) to make the classification and detection comparatively better. The equivariance characteristics of CapsNet make it more influential as it discourages the effect of any structural invariance of an input image on the network.