Knowledge based fuzzy c-means method for rapid brain tissues segmentation of magnetic resonance imaging scans with CUDA enabled GPU machine

Valsalan, Prajoona; Sriramakrishnan, P.; Sridhar, S.; Latha, G. Charlyn Pushpa; Priya, A.; Ramkumar, S.; Singh, Anshuman; Rajendran, T.

doi:10.1007/s12652-020-02132-6

Cited by 49 publications

(12 citation statements)

References 29 publications

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“…It has the network data with and without attacks, which was approximately the real data of the network. [22][23][24][25][26][27][28][29][30][31][32][33] This dataset was uneven, hence this dataset with the duplicating method as it basically affects the training of the proposed model, and then the testing was performed. This research was experimented using Keras on the Tensorflow package on 64-bit Intel Core-i5 processor with 8 GB RAM in Windows 8 system.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Retracted: Intrusion detection in cloud environment using hybrid genetic algorithm and back propagation neural network

Manimurugan

Manimegalai

Valsalan

et al. 2020

Int J Communication

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The security for cloud network systems is essential and significant to secure the data source from intruders and attacks. Implementing an intrusion detection system (IDS) for securing from those intruders and attacks is the best option. Many IDS models are presently based on different techniques and algorithms like machine learning and deep learning. In this research, an IDS for the cloud computing environment is proposed. Here in this model, the genetic algorithm (GA) and back propagation neural network (BPNN) are used for attack detection and classification. The Canadian Institute for Cybersecurity CIC-IDS 2017 dataset is used for the evaluation of performance analysis.Initially, from the dataset, the data are preprocessed, and by using the genetic algorithm, the attack was detected. The detected attacks are classified using the BPNN classifier for identifying the types of attacks. The performance analysis was executed, and the results are obtained and compared with the existing machine learning-based classifiers like FC-ANN, NB-RF, KDBN, and FCM-SVM techniques. The proposed GA-BPNN model performs better in every performance metric, like accuracy, precision, recall, and detection rate.Overall, from the performance analysis, the best classification accuracy is achieved for Web attack detection with 97.90%, and the best detection rate is achieved for Brute force attack detection with 97.89%.

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Section: Dataset Descriptionmentioning

confidence: 99%

Retracted: Intrusion detection in cloud environment using hybrid genetic algorithm and back propagation neural network

Manimurugan

Manimegalai

Valsalan

et al. 2020

Int J Communication

Self Cite

View full text Add to dashboard Cite

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“…Since the AR system was lacking such a GPU, this computation had to be performed on a computer. The abovementioned CUDA API makes use of the parallelism available in a GPU [ 25 , 26 ], such as CNN operations, to speed up computations [ 27 ]. This application was developed using Qt (version 5.15) to create the Graphical User Interface (GUI), and OpenCV (version 4.5.5) was utilized to conduct US image processing and lesion segmentation.…”

Section: Methodsmentioning

confidence: 99%

Augmented Reality-Assisted Ultrasound Breast Biopsy

Costa

Ferreira

Araújo

et al. 2023

Sensors

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Breast cancer is the most prevalent cancer in the world and the fifth-leading cause of cancer-related death. Treatment is effective in the early stages. Thus, a need to screen considerable portions of the population is crucial. When the screening procedure uncovers a suspect lesion, a biopsy is performed to assess its potential for malignancy. This procedure is usually performed using real-time Ultrasound (US) imaging. This work proposes a visualization system for US breast biopsy. It consists of an application running on AR glasses that interact with a computer application. The AR glasses track the position of QR codes mounted on an US probe and a biopsy needle. US images are shown in the user’s field of view with enhanced lesion visualization and needle trajectory. To validate the system, latency of the transmission of US images was evaluated. Usability assessment compared our proposed prototype with a traditional approach with different users. It showed that needle alignment was more precise, with 92.67 ± 2.32° in our prototype versus 89.99 ± 37.49° in a traditional system. The users also reached the lesion more accurately. Overall, the proposed solution presents promising results, and the use of AR glasses as a tracking and visualization device exhibited good performance.

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“…Moreover, several groups have proven the efficiency and acceleration of fuzzy c-means and fuzzy c-means based segmentations when they are implemented on hybrid CPU-GPU designs. This way, promising results have been obtained in different parts of the body, such as brain and breast [42,43].…”

Section: Cpu Architectures In Medical Imagingmentioning

confidence: 98%

Hardware Architectures for Real-Time Medical Imaging

et al. 2021

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Medical imaging is considered one of the most important advances in the history of medicine and has become an essential part of the diagnosis and treatment of patients. Earlier prediction and treatment have been driving the acquisition of higher image resolutions as well as the fusion of different modalities, raising the need for sophisticated hardware and software systems for medical image registration, storage, analysis, and processing. In this scenario and given the new clinical pipelines and the huge clinical burden of hospitals, these systems are often required to provide both highly accurate and real-time processing of large amounts of imaging data. Additionally, lowering the prices of each part of imaging equipment, as well as its development and implementation, and increasing their lifespan is crucial to minimize the cost and lead to more accessible healthcare. This paper focuses on the evolution and the application of different hardware architectures (namely, CPU, GPU, DSP, FPGA, and ASIC) in medical imaging through various specific examples and discussing different options depending on the specific application. The main purpose is to provide a general introduction to hardware acceleration techniques for medical imaging researchers and developers who need to accelerate their implementations.

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Knowledge based fuzzy c-means method for rapid brain tissues segmentation of magnetic resonance imaging scans with CUDA enabled GPU machine

Cited by 49 publications

References 29 publications

Retracted: Intrusion detection in cloud environment using hybrid genetic algorithm and back propagation neural network

Retracted: Intrusion detection in cloud environment using hybrid genetic algorithm and back propagation neural network

Augmented Reality-Assisted Ultrasound Breast Biopsy

Hardware Architectures for Real-Time Medical Imaging

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