scholarly journals Automatic classification of medical image modality and anatomical location using convolutional neural network

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253205
Author(s):  
Chen-Hua Chiang ◽  
Chi-Lun Weng ◽  
Hung-Wen Chiu
Keyword(s):  
Medical Image ◽  
Brain Mri ◽  
Image Data ◽  
Anatomical Location ◽  
Patient Demographics ◽  
Group Format ◽  
Abdomen Ct ◽  
And Storage ◽  
Image Modality

Modern radiologic images comply with DICOM (digital imaging and communications in medicine) standard, which, upon conversion to other image format, would lose its image detail and information such as patient demographics or type of image modality that DICOM format carries. As there is a growing interest in using large amount of image data for research purpose and acquisition of large amount of medical image is now a standard practice in the clinical setting, efficient handling and storage of large amount of image data is important in both the clinical and research setting. In this study, four classes of images were created, namely, CT (computed tomography) of abdomen, CT of brain, MRI (magnetic resonance imaging) of brain and MRI of spine. After converting these images into JPEG (Joint Photographic Experts Group) format, our proposed CNN architecture could automatically classify these 4 groups of medical images by both their image modality and anatomic location. We achieved excellent overall classification accuracy in both validation and test sets (> 99.5%), specificity and F1 score (> 99%) in each category of this dataset which contained both diseased and normal images. Our study has shown that using CNN for medical image classification is a promising methodology and could work on non-DICOM images, which could potentially save image processing time and storage space.

scholarly journals Transfer Learning for Classification of 2D Brain MRI Images and Tumor Segmentation

2020 ◽  
Vol 8 (6) ◽  
pp. 2016-2019
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Medical Image ◽  
Brain Mri ◽  
Acquisition Time ◽  
Tumor Segmentation ◽  
Exact Location ◽  
Arduous Task

The focus of the paper is to classify the images into tumorous and non-tumorous and then locate the tumor. Amongst many medical imaging applications segmentation of Brain Tumors is an important and arduous task as the data acquired is disrupted due to artifacts being produced and acquisition time being very less, so classifying and finding the exact location of tumor is one of the most important jobs. In the paper, deep learning specifically the convolutional neural network is used to demonstrate its potential for image classification task. As the learning from available dataset will be low, so we use transfer learning [4] approach, as it is a developing AI strategy that overwhelms with the best outcomes on several image classification assignments because the pre-trained models have gained good knowledge about the features by training on a large number of images. Since, medical image datasets are hard to collect so transfer learning (Alexnet) [1] is used. Later on, after successful classification the aim is to find the exact location of the tumor and this is achieved using basics of image processing inspired by well-known technique of Mask R-CNN [9].

scholarly journals Scene classification of remote sensing image based on compound pruning

2021 ◽  
Vol 336 ◽  
pp. 06030
Author(s):  
Fengbing Jiang ◽  
Fang Li ◽  
Guoliang Yang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Data ◽  
Remote Sensing Image ◽  
Convolution Neural Network ◽  
Model Parameters ◽  
Scene Classification ◽  
Remote Sensing Image Classification ◽  
And Storage

Convolution neural network for remote sensing image scene classification consumes a lot of time and storage space to train, test and save the model. In this paper, firstly, elastic variables are defined for convolution layer filter, and combined with filter elasticity and batch normalization scaling factor, a compound pruning method of convolution neural network is proposed. Only the superparameter of pruning rate needs to be adjusted during training. in the process of training, the performance of the model can be improved by means of transfer learning. In this paper, algorithm tests are carried out on NWPU-RESISC45 remote sensing image data to verify the effectiveness of the proposed method. According to the experimental results, the proposed method can not only effectively reduce the number of model parameters and computation, but also ensure the accuracy of the algorithm in remote sensing image classification.

scholarly journals Deniable Authentication Encryption for Privacy Protection using Blockchain

2021 ◽  
Vol 3 (3) ◽  
pp. 259-271
Author(s):  
C. Vijesh Joe ◽  
Jennifer S. Raj
Keyword(s):  
Privacy Protection ◽  
Medical Image ◽  
Image Data ◽  
Security And Privacy ◽  
Authenticated Encryption ◽  
Computing Power ◽  
Plain Image ◽  
Tampered Image ◽  
Cloud Applications ◽  
And Storage

Cloud applications that work on medical data using blockchain is used by managers and doctors in order to get the image data that is shared between various healthcare institutions. To ensure workability and privacy of the image data, it is important to verify the authenticity of the data, retrieve cypher data and encrypt plain image data. An effective methodology to encrypt the data is the use of a public key authenticated encryption methodology which ensures workability and privacy of the data. But, there are a number of such methodologies available that have been formulated previously. However, the drawback with those methodologies is their inadequacy in protecting the privacy of the data. In order to overcome these disadvantages, we propose a searchable encryption algorithm that can be used for sharing blockchain- based medical image data. This methodology provides traceability, unforgettable and non-tampered image data using blockhain technology, overcoming the drawbacks of blockchain such as computing power and storage. The proposed work will also sustain keyword guessing attacks apart from verification of authenticity and privacy protection of the image data. Taking these factors into consideration, it is determine that there is much work involved in providing stronger security and protecting privacy of data senders. The proposed methodology also meets the requirement of indistinguishability of trapdoor and ciphertext. The highlights of the proposed work are its capability in improving the performance of the system in terms of security and privacy protection.

scholarly journals An optimized approach for extensive segmentation and classification of brain MRI

2020 ◽  
Vol 10 (3) ◽  
pp. 2392
Author(s):  
Harish S ◽  
G.F Ali Ahammed
Keyword(s):  
Medical Image ◽  
Significant Contribution ◽  
Brain Mri ◽  
Health Condition ◽  
Abnormality Detection ◽  
Classification Problems ◽  
Segmentation Process ◽  
Robust Modelling ◽  
Effective Diagnosis

With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.

Four-Corner Arthrodesis versus Proximal Row Carpectomy: Risk Factors and Complications Associated with Prolonged Postoperative Opioid Use

2020 ◽  
Author(s):  
Neill Y. Li ◽  
Alexander S. Kuczmarski ◽  
Andrew M. Hresko ◽  
Avi D. Goodman ◽  
Joseph A. Gil ◽  
...  
Keyword(s):  
Risk Factors ◽  
International Classification Of Diseases ◽  
Opioid Use ◽  
Proximal Row Carpectomy ◽  
Use Patterns ◽  
Patient Demographics ◽  
Implant Complications ◽  
Increased Risk ◽  
Classification Of Diseases

Abstract Introduction This article compares opioid use patterns following four-corner arthrodesis (FCA) and proximal row carpectomy (PRC) and identifies risk factors and complications associated with prolonged opioid consumption. Materials and Methods The PearlDiver Research Program was used to identify patients undergoing primary FCA (Current Procedural Terminology [CPT] codes 25820, 25825) or PRC (CPT 25215) from 2007 to 2017. Patient demographics, comorbidities, perioperative opioid use, and postoperative complications were assessed. Opioids were identified through generic drug codes while complications were defined by International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification codes. Multivariable logistic regressions were performed with p < 0.05 considered statistically significant. Results A total of 888 patients underwent FCA and 835 underwent PRC. Three months postoperatively, more FCA patients (18.0%) continued to use opioids than PRC patients (14.7%) (p = 0.033). Preoperative opioid use was the strongest risk factor for prolonged opioid use for both FCA (odds ratio [OR]: 4.91; p < 0.001) and PRC (OR: 6.33; p < 0.001). Prolonged opioid use was associated with an increased risk of implant complications (OR: 4.96; p < 0.001) and conversion to total wrist arthrodesis (OR: 3.55; p < 0.001) following FCA. Conclusion Prolonged postoperative opioid use is more frequent in patients undergoing FCA than PRC. Understanding the prevalence, risk factors, and complications associated with prolonged postoperative opioid use after these procedures may help physicians counsel patients and implement opioid minimization strategies preoperatively.

MODERN PROBLEMATICS AND ANALYSIS OF EFFICIENCY OF APPLICATION OF DIFFERENT APPROACHES TO DESTRUCTION OF INFORMATION ON MAGNETIC MEDIA

2019 ◽  
Vol 2 (12) ◽  
pp. 154-164
Author(s):  
O. Semenenko ◽  
O. Vodchyts ◽  
V. Koverga ◽  
R. Lukash ◽  
O. Lutsenko
Keyword(s):  
Armed Forces ◽  
Information Storage ◽  
Storage Device ◽  
Magnetic Media ◽  
Long Term Storage ◽  
Physical Impact ◽  
And Storage ◽  
Term Storage

The introduction and active use of information transmission and storage systems in the Ministry of Defense (MoD) of Ukraine form the need to develop ways of guaranteed removal of data from media after their use or long-term storage. Such a task is an essential component of the functioning of any information security system. The article analyzes the problems of guaranteed destruction of information on magnetic media. An overview of approaches to the guaranteed destruction of information on magnetic media of different types is presented, and partial estimates of the effectiveness of their application are given by some generally accepted indicators of performance evaluation. The article also describes the classification of methods of destruction of information depending on the influence on its medium. The results of the analysis revealed the main problems of application of software methods and methods of demagnetization of the information carrier. The issue of guaranteed destruction of information from modern SSD devices, which are actively used in the formation of new systems of information accumulation and processing, became particularly relevant in the article. In today's conditions of development of the Armed Forces of Ukraine, methods of mechanical and thermal destruction are more commonly used today. In the medium term, the vector of the use of information elimination methods will change towards the methods of physical impact by the pulsed magnetic field and the software methods that allow to store the information storage device, but this today requires specialists to develop new ways of protecting information in order to avoid its leakage.

Sign in / Sign up

Export Citation Format

Share Document