A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics.

Deep Learning (DL) drives academics to create models for cancer diagnosis using medical image processing because of its innate ability to recognize difficult-to-detect patterns in complex, noisy, and massive data. The use of deep learning algorithms for real-time cancer diagnosis is explored in depth in this work. Real-time medical diagnosis determines the illness or condition that accounts for a patient's symptoms and outward physical manifestations within a predetermined time frame.

DPro-SM - A distributed framework for proactive straggler mitigation using LSTM.

The recent advancement in deep learning with growth in big data and high-performance computing is Distributed Deep Learning. The rapid rise in the volume of data and network complexity has led to significant growth in DDL. Distribution of the network leads to high communication and computation among the nodes, which leads to high training time and lower accuracy.

Health Care Equity Through Intelligent Edge Computing and Augmented Reality/Virtual Reality: A Systematic Review.

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues.

Real-time pneumonia prediction using pipelined spark and high-performance computing.

Background: Pneumonia is a respiratory disease caused by bacteria; it affects many people, particularly in impoverished countries where pollution, unclean living standards, overpopulation, and insufficient medical infrastructures are prevalent. To guarantee curative therapy and boost survival chances, it is vital to detect pneumonia soon enough. Imaging using chest X-rays is the most common way of detecting pneumonia.

Effect of neural network structure in accelerating performance and accuracy of a convolutional neural network with GPU/TPU for image analytics.

Background: In deep learning the most significant breakthrough in the field of image recognition, object detection language processing was done by Convolutional Neural Network (CNN). Rapid growth in data and neural networks the performance of the DNN algorithms depends on the computation power and the storage capacity of the devices.

Methods: In this paper, the convolutional neural network used for various image applications was studied and its acceleration in the various platforms like CPU, GPU, TPU was done.