A CNN pruning approach using constrained binary particle swarm optimization with a reduced search space for image classification

Tmamna, Jihene; Ben Ayed, Emna; Fourati, Rahma; Hussain, Amir; Ben Ayed, Mounir

doi:10.1016/j.asoc.2024.111978

Applied Soft Computing

2024

DOI: 10.1016/j.asoc.2024.111978

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A CNN pruning approach using constrained binary particle swarm optimization with a reduced search space for image classification

Jihene Tmamna,

Emna Ben Ayed,

Rahma Fourati

et al.

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2024

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Cited by 3 publications

References 38 publications

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A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs

Tmamna,

Fourati,

Ayed

et al. 2024

Neurocomputing

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A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs

Tmamna,

Fourati,

Ayed

et al. 2024

Neurocomputing

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Alignment of multimodal rigid cardiac angiography images with an improved particle swarm algorithm

Wang,

Zhang

2024

Applied Mathematics and Nonlinear Sciences

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In this paper, we conduct a preliminary study on the current development status in medical image alignment and build up a basic framework for image alignment. The feature space, search space, similarity measure, and search strategy of cardiac angiography images are calculated and studied. The DGVF model is utilized to process the traditional snake model for optimization search and is combined with B-splines to construct the B-spline DGVF model. Optimize the traditional MsFCM algorithm by using the PSO algorithm and propose an MsFCM-PSO image segmentation method. It is applied together with the B-spline DGVF model to segment the vascular lumen in cardiac angiography ultrasound images. Finally, the model of this paper is analyzed in terms of segmentation performance, alignment stability, and evaluation of alignment results. The mean values of Dice, IoU, and HD of this paper’s MsFCM-PSO model in image segmentation of cardiac vessels are 94.27%, 92.60%, and 1.06, respectively (all optimal performances). In the ablation experiments, the MsFCMPSO model in this paper shows an increase of 6.02% and 5.47% in the mean values of Dice and IoU compared to the benchmark model. The stability calibration percentage of this paper’s MsFCM-PSO algorithm is 31.13% when the Gaussian factor is 0.5, which is significantly better than other algorithms. The algorithm in this paper is better than other methods in terms of alignment stability and alignment results.

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Pruning Policy for Image Classification Problems Based on Deep Learning

Pachon,

Pinzon-Arenas,

Ballesteros

2024

Informatics

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In recent years, several methods have emerged for compressing image classification models using CNNs, for example, by applying pruning to the convolutional layers of the network. Typically, each pruning method uses a type of pruning distribution that is not necessarily the most appropriate for a given classification problem. Therefore, this paper proposes a methodology to select the best pruning policy (method + pruning distribution) for a specific classification problem and global pruning rate to obtain the best performance of the compressed model. This methodology was applied to several image datasets to show the influence not only of the method but also of the pruning distribution on the quality of the pruned model. It was shown that the selected pruning policy affects the performance of the pruned model to different extents, and that it depends on the classification problem to be addressed. For example, while for the Date Fruit Dataset, variations of more than 10% were obtained, for CIFAR10, variations were less than 5% for the same cases evaluated.

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A CNN pruning approach using constrained binary particle swarm optimization with a reduced search space for image classification

Cited by 3 publications

References 38 publications

A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs

A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs

Alignment of multimodal rigid cardiac angiography images with an improved particle swarm algorithm

Pruning Policy for Image Classification Problems Based on Deep Learning

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