An Efficient Brain MR Images Segmentation Hardware Using Kernel Fuzzy C-Means

Shoeibi, Afshin; Ghassemi, Navid; Hosseini-Nejad, Hossein; Rouhani, Modjtaba

doi:10.1109/icbme49163.2019.9030419

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Among these platforms, the FPGA provides an especially high-speed hardware solution that can be customized according to the algorithm's specifications. Afshin introduced a hardware solution approach allowing the FCM to be utilized for brain MR image segmentation [59]. In this case, the Xilinx FPGA Virtex7 was used with the Modelsim tool to obtain the simulation results.…”

Section: Related Workmentioning

confidence: 99%

FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology

Almomany

Jarrah

Assaf

2022

Journal of Electrical and Computer Engineering

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Fuzzy C-Means (FCM) is a widely used clustering algorithm that performs well in various scientific applications. Implementing FCM involves a massive number of computations, and many parallelization techniques based on GPUs and multicore systems have been suggested. In this study, we present a method for optimizing the FCM algorithm for high-speed field-programmable gate technology (FPGA) using a high-level C-like programming language called open computing language (OpenCL). The method was designed to enable the high-level compiler/synthesis tool to manipulate a task-parallelism model and create an efficient design. Our experimental results (based on several datasets) show that the proposed method makes the FCM execution time more than 186 times faster than the conventional design running on a single-core CPU platform. Also, its processing power reached 89 giga floating points operations per second (GFLOPs).

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Section: Related Workmentioning

confidence: 99%

FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology

Almomany

Jarrah

Assaf

2022

Journal of Electrical and Computer Engineering

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“…Manual segmentation of medical images takes much time; thus, applying machine learning models is crucially paramount. Among the most important segmentation models, the various types of fuzzy clustering methods [26], [27] and DL procedures such as U-Net [28] can be expressed. In the CADS, with the segmentation approach, patients' CT-Scan images and their manual segments labeled by doctors are fed to the DL network.…”

Section: Computer Aided Diagnosis System (Cads) For Covid-19 Detectionmentioning

confidence: 99%

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

Shoeibi¹,

Khodatars²,

Alizadehsani³

et al. 2020

Preprint

Self Cite

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Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed.

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“…The AI techniques, specifically deep learning techniques, can handle this process more effectively than manual segmentation, which can be time-consuming. Among the most crucial segmentation methods, U-Net [12] and fuzzy clustering methods [13,14] have produced empirical results with high performance. The input of deep learning models is patients' images and their corresponding segments that have been manually labeled by the doctors.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder

Dhahri¹,

Rabhi²,

Chelbi³

et al. 2021

Computers, Materials &Amp; Continua

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The exponential increase in new coronavirus disease 2019 cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and COVID-19. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model's architecture mainly composed of eight autoencoders, which were fed to two dense layers and SoftMax classifiers. The proposed model was evaluated with 6356 images from the datasets from different sources. The experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting, respectively. The metrics used for the SDCA model were the classification accuracy, precision, sensitivity, and specificity for both schemes. Our results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%. Therefore, this model can help physicians accelerate COVID-19 diagnosis.

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An Efficient Brain MR Images Segmentation Hardware Using Kernel Fuzzy C-Means

Cited by 6 publications

References 12 publications

FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology

FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder

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