Parallel Implementation and Performance Evaluation of some Supervised Clustering Algorithms for MRI Images Segmentation

Ali, Noureddine Ait; Cherradi, Bouchaib; Abbassi, Ahmed El; Bouattane, Omar; Youssfi, Mohamed

doi:10.1145/3372938.3373007

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…In [7], the authors present an integrated view of the methods used in hyperparametric optimization of learning systems. it emphasizes the hyperparametric optimization aspects by using a combination of techniques for: optimization, space research and reduction of training time.…”

Section: Literature Surveymentioning

confidence: 99%

Optimization of Machine Learning Algorithms Hyper-Parameters for Improving the Prediction of Patients Infected with COVID-19

Hamida

Gannour

Cherradi

et al. 2020

2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS)

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In the modeling domain, the selection of appropriate hyper-parameters for classification or prediction algorithms is a difficult task, which has an impact on generalization capacity and classifier performance. In this paper, we compared the performance of five Machine Learning (ML) algorithms from different categories namely: SVM, AdaBoost, Random Forest, XGBoost and Decision Tree. In the first experiment, we adopt a default setting of each model for training and testing. In the second experiment, we use the GridSearch function to find an optimal configuration of the model. The experiments are performed on dataset of anonymous patients with or without COVID-19 disease. The used dataset is obtained from the Albert Einstein Hospital in Sao Paulo, Brazil. To evaluate the reached results, we used different performance evaluation metrics such as: accuracy, precision, recall, AUC and F1-score. The results of the proposed approach have shown that the optimization of the hyperparameters of the studied learning models leads to an improvement of 18% in terms of Recall.

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Section: Literature Surveymentioning

confidence: 99%

Optimization of Machine Learning Algorithms Hyper-Parameters for Improving the Prediction of Patients Infected with COVID-19

Hamida

Gannour

Cherradi

et al. 2020

2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS)

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“…Moreover their high computing capabilities Graphics Processing Units (GPU) are more adapted to image processing techniques [ 20 , 29 , 35 ]. Clustering algorithms not only can benefit from the parallel behavior of GPU processors but also can provide interesting performances [ 2 , 6 , 7 , 49 ].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation

Ali

Abbassi

Bouattane

2022

Multimed Tools Appl

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Image processing by segmentation technique is an important phase in medical imaging such as MRI. Its objective is to analyze the different tissues in human body. In research area, Fuzzy set is one of the most successful techniques that guarantees a robust classification. Spatial FCM (SFCM); one of the fuzzy c-means variants; considers spatial information to deal with the noisy images. To reduce this iterative algorithm’s execution time, a hard SIMD architecture has been planted named the Graphical Processing Unit (GPU). In this work, a great contribution has been done to diagnose, confront and implement three different parallel implementations on GPU. A parallel implementations’ extensive study of SFCM entitled PSFCM using 3 × 3 window is presented, and the experiments illustrate a significant decrease in terms of running time of this algorithm known by its high complexity. The experimental results indicate that the parallel version’s execution time is about 9.46 times faster than the sequential implementation on image segmentation. This gain in terms of speed-up is achieved on the Nvidia GeForce GT 740 m GPU.

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“…It makes computers capable of solving many pattern recognition and object extraction problems using datasets of 2D or 3D images. In case of 3D MRI images classification task [4], [5] it requires a huge processing capacity which can be bypassed by adopting a parallel algorithms [6]. In [7], an example of the above parallel approach where the authors propose a parallel c-mean algorithm applied to MRI images classification showing good time complexity on its results.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images

Moujahid¹,

Cherradi²,

Gannour³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Analysis and classification of lung diseases using X-ray images is a primary step in the procedure of pneumonia diagnosis, especially in a critical period as pandemic of COVID-19 that is type of pneumonia. Therefore, an automatic method with high accuracy of classification is needed to perform classification of lung diseases due to the increasing number of cases. Convolutional Neural Networks (CNN) based classification has gained a big popularity over the last few years because of its speed and level of accuracy on the image's classification tasks. Through this article, we propose an implementation a CNNbased classification models using transfer learning technique to perform pneumonia detection and compare the results in order to detect the best model for the task according to certain parameters. As this has become a fast expanding field, there are several models but we will focus on the best outperforming algorithms according to their architecture, length and type of layers and evaluation parameters for the classification tasks. Firstly, we review the existing conventional methods and deep learning architectures used for segmentation in general. Next, we perform a deep performance and analysis based on accuracy and loss function of implemented models. A critical analysis of the results is made to highlight all important issues to improve.

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Parallel Implementation and Performance Evaluation of some Supervised Clustering Algorithms for MRI Images Segmentation

Cited by 5 publications

References 14 publications

Optimization of Machine Learning Algorithms Hyper-Parameters for Improving the Prediction of Patients Infected with COVID-19

Optimization of Machine Learning Algorithms Hyper-Parameters for Improving the Prediction of Patients Infected with COVID-19

Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation

Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images

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