Implementation of a Medical Image File Accessing System on Cloud Computing

2010 ◽  
Author(s):  
Chao-Tung Yang ◽  
Lung-Teng Chen ◽  
Wei-Li Chou ◽  
Kuan-Chieh Wang
Keyword(s):  
Cloud Computing ◽  
Medical Image ◽  
Image File

scholarly journals Virtual Disks Performance Analysis Using Flexible I/O and Powerstat

2021 ◽  
Vol 9 (10) ◽  
pp. 267-296
Author(s):  
Pritam Patange
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Disk File ◽  
Virtual Machine Monitor ◽  
Image File ◽  
File Formats ◽  
Physical Infrastructure ◽  
Disk Image ◽  
The Impact

Abstract: Cloud computing has experienced significant growth in the recent years owing to the various advantages it provides such as 24/7 availability, quick provisioning of resources, easy scalability to name a few. Virtualization is the backbone of cloud computing. Virtual Machines (VMs) are created and executed by a software called Virtual Machine Monitor (VMM) or the hypervisor. It separates compute environments from the actual physical infrastructure. A disk image file representing a single virtual machine is created on the hypervisor’s file system. In this paper, we analysed the runtime performance of multiple different disk image file formats. The analysis comprises of four different parameters of performance namely- bandwidth, latency, input-output operations performed per second (IOPS) and power consumption. The impact of the hypervisor’s block and file sizes is also analysed for the different file formats. The paper aims to act as a reference for the reader in choosing the most appropriate disk file image format for their use case based on the performance comparisons made between different disk image file formats on two different hypervisors – KVM and VirtualBox. Keywords: Virtualization, Virtual disk formats, Cloud computing, fio, KVM, virt-manager, powerstat, VirtualBox.

Managing multiple medical image file formats and conventions

2003 ◽  
Author(s):  
Scott C. Neu ◽  
Daniel J. Valentino ◽  
Keith R. Ouellette ◽  
Arthur W. Toga
Keyword(s):  
Medical Image ◽  
Image File ◽  
File Formats

Accessing medical image file with co-allocation HDFS in cloud

2015 ◽  
Vol 43-44 ◽  
pp. 61-73 ◽  
Author(s):  
Chao-Tung Yang ◽  
Wen-Chung Shih ◽  
Lung-Teng Chen ◽  
Cheng-Ta Kuo ◽  
Fuu-Cheng Jiang ◽  
...  
Keyword(s):  
Medical Image ◽  
Image File

MIFAS: Medical Image File Accessing System in Co-allocation Data Grids

2008 ◽  
Author(s):  
Chao-Tung Yang ◽  
Chiu-Hsiung Chen ◽  
Ming-Feng Yang ◽  
Wen-Chung Chiang
Keyword(s):  
Medical Image ◽  
Data Grids ◽  
Image File

scholarly journals Medical Image File Formats

2013 ◽  
Vol 27 (2) ◽  
pp. 200-206 ◽  
Author(s):  
Michele Larobina ◽  
Loredana Murino
Keyword(s):  
Medical Image ◽  
Image File ◽  
File Formats

scholarly journals Security and Privacy of Cloud- and IoT-Based Medical Image Diagnosis Using Fuzzy Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
J. Deepika ◽  
C. Rajan ◽  
T. Senthil
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Convolutional Neural Network ◽  
Data Storage ◽  
Medical Image ◽  
Disease Classification ◽  
Security And Privacy ◽  
Medical Image Diagnosis ◽  
Image Diagnosis

In recent times, security in cloud computing has become a significant part in healthcare services specifically in medical data storage and disease prediction. A large volume of data are produced in the healthcare environment day by day due to the development in the medical devices. Thus, cloud computing technology is utilised for storing, processing, and handling these large volumes of data in a highly secured manner from various attacks. This paper focuses on disease classification by utilising image processing with secured cloud computing environment using an extended zigzag image encryption scheme possessing a greater tolerance to different data attacks. Secondly, a fuzzy convolutional neural network (FCNN) algorithm is proposed for effective classification of images. The decrypted images are used for classification of cancer levels with different layers of training. After classification, the results are transferred to the concern doctors and patients for further treatment process. Here, the experimental process is carried out by utilising the standard dataset. The results from the experiment concluded that the proposed algorithm shows better performance than the other existing algorithms and can be effectively utilised for the medical image diagnosis.

Sign in / Sign up

Export Citation Format

Share Document