Literature review: efficient deep neural networks techniques for medical image analysis

Significant evolution in deep learning took place in 2010, when software developers started using graphical processing units for general-purpose applications. From that date, the deep neural network (DNN) started progressive steps across different applications ranging from natural language processing to hyperspectral image processing. The convolutional neural network (CNN) mostly triggers the interest, as it is considered one of the most powerful ways to learn useful representations of images and other structured data. The revolution of DNNs in medical imaging (MI) came in 2012, when Li launched ImageNet, a free database of more than 14 million labeled medical images. This state-of-the-art work presents a comprehensive study for the recent DNNs research directions applied in MI analysis. Clinical and pathological analysis through a selected patch of most cited researches is introduced. It will be shown how DNNs are able to tackle medical problems: classification, detection, localization, segmentation, and automatic diagnosis. Datasets comprises a range of imaging technologies: X-Ray, MRI, CT, Ultrasound, PET, Fluorescene Angiography, and even photographic images. This work surveys different patterns of DNNs and focuses somehow on the CNN, which offers an outstanding percentage of solutions compared to other DNNs structures. CNN emphasizes image features and has well-known architectures. On the other hand, limitations beyond DNNs training and execution time will be explained. Problems related to data augmentation and image annotation will be analyzed among a multiple of high standard publications. Finally, a comparative study of existing software frameworks supporting DNNs and future research directions in the area will be presented. From all presented works it could be deduced that the use of DNNs in healthcare is still in its early stages, there are strong initiatives in academia and industry to pursue healthcare projects based on DNNs.