A Two-level Cloud Storage System Based on Asynchronous Message for Medical Image Big Data

Li, Wei; Feng, Chaolu; Jin, Ci; Chen, Qiang; Liu, Haining; Zhao, Dazhe

doi:10.1109/bigcom.2019.00017

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The work to parallelize the MR brain data through multinodes increases the speed and efficiency of the system. 14,22,23 The parallel distributed processing of the bulk data is tackled both using Hadoop and Matlab distributed computing servers to increase the computation nodes for flowing the data. Research forthcoming dealing with the real application in parallel fashion for big data analytic has covered the issues of poor runtime.…”

Section: Related Workmentioning

confidence: 99%

An efficient Hadoop‐based brain tumor detection framework using big data analytic

Chahal

Pandey

2020

Softw Pract Exp

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The exponential increase of brain MR image data in the medical imaging field requires faster and accurate segmentation of tumor. The computer aided detection systems acting as a second option to experts, radiologists, and surgeons needs to be swift enough to handle parallelism. However, handling of massive MR data for segmentation with high accuracy and low processing time is significant concern of any framework. In this article, distributed platforms for brain tumor segmentation using hybrid weighted fuzzy approach integrated with Matlab Distributed Computing Server and Hadoop has been proposed. The approach is based on the fuzzification of the pixel values to achieve more meaningful clusters by grouping of large data into similar clusters. The article focuses on analyzing the performance of varying sized data sets using hybrid fuzzy clustering in MapReduce on Hadoop to deal with huge MR brain data cross clusters of commodity computers. For experimentation varying size of DICOM data set is processed through different number of clusters to compare the read, write, and processing time on each node. The read and write operation time elevates as the data size increasing is floated to multinode. However, the processing time of the proposed approach turns to be 35 min on single, whereas 3-node clusters process the same data set (215 MB) in 3.4 min. Furthermore, increasing the data set to 7.3 GB the 3-node cluster performs in 235.4 min which is greatly reduced from single node processing time of 2085.2 min.

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Section: Related Workmentioning

confidence: 99%

An efficient Hadoop‐based brain tumor detection framework using big data analytic

Chahal

Pandey

2020

Softw Pract Exp

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“…Recently, the research is moving towards the classification of medical images from the big data environment, which is not handled by classical models. The method of parallelizing the medical images through multiple nodes boosts the speed and competence of the system [6][7][8]. The parallel distributed processing of the massive quantity of data is handled by the use of Hadoop and MATLAB distributed computing server for raising the computational complexity of the nodes for data flow.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Optimization Based Multi-Key Enabled Encryption With Image Classification for Big Data Environment in Healthcare Sector

Kumar¹,

Mohan²,

Nagesh³

2021

INDJCSE

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In recent times, medical imaging domain undergoes significant development with respect to innovations, market growth, and exploitation with the rise in the generation of massive data quantity poses diagnostic imaging in the context of Big data. At the same time, securing medical images is needed and it remains a crucial process on the shared communication model. Encryption is considered an effective way of securing the data transmission process in big data environment. Besides, the computer aided diagnosis also provides a second opinion to professionals to manage parallelism. So, it becomes essential to design digital environments and applications which offer effective handling of medical images such as Big data. This paper presents a new Metaheuristic Optimization based Multi-Key Enabled Encryption with Image Classification (MOMEE-IC) for Big Data Environment in Healthcare Sector. The proposed model involves two major operations as encryption and image classification. Firstly, the encryption process involves Multiple key based Homomorphic Encryption (MHE) with lion optimization algorithm (LOA) based optimal key generation process, called MHE-LOA. Besides, Stacked Denoise Autoencoder with Logistic Regression (SDAE-LR) based image classification process is employed for diagnosing the images in the cloud platform. To ensure the superior results of the presented model, a wide set of simulations were performed and the results are examined under distinct aspects.

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Efficient Data Compression Algorithms for Cloud-Based Medical Imaging

Sharma,

Pundir,

Deepak

et al. 2023

2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)

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A Two-level Cloud Storage System Based on Asynchronous Message for Medical Image Big Data

Cited by 3 publications

References 7 publications

An efficient Hadoop‐based brain tumor detection framework using big data analytic

An efficient Hadoop‐based brain tumor detection framework using big data analytic

Metaheuristic Optimization Based Multi-Key Enabled Encryption With Image Classification for Big Data Environment in Healthcare Sector

Efficient Data Compression Algorithms for Cloud-Based Medical Imaging

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