Metaheuristics Applied to Blood Image Analysis

Monteiro, Ana Carolina Borges; França, Reinaldo Padilha; Estrela, Vania V.; Razmjooy, Navid; Iano, Yuzo; Negrete, Pablo David Minango

doi:10.1007/978-3-030-56689-0_6

Cited by 3 publications

(2 citation statements)

References 67 publications

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“…The simulation data was used to fit the electric stack model through the neural network. Various operating circumstances' impacts on performance of system and stack power of the PEMFC were analyzed [ 17 ]. In order to combine performance of the system and reactor's power for expressing combined efficiency index, an algorithm was presented, called Particle Swarm Optimization.…”

Section: Introductionmentioning

confidence: 99%

PEMFC model identification using a squeezenet developed by modified transient search optimization algorithm

Duan,

Lin,

Fathi

2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

PEMFC model identification using a squeezenet developed by modified transient search optimization algorithm

Duan,

Lin,

Fathi

2024

Heliyon

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“…Image processing is a substantial part of medical research/clinical practice [ 1 , 2 ]. In the last few years, medical image analysis has been highly developed by enhancing digital imaging methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Pourasad

Cavallaro

2021

IJERPH

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At present, there is an increase in the capacity of data generated and stored in the medical area. Thus, for the efficient handling of these extensive data, the compression methods need to be re-explored by considering the algorithm’s complexity. To reduce the redundancy of the contents of the image, thus increasing the ability to store or transfer information in optimal form, an image processing approach needs to be considered. So, in this study, two compression techniques, namely lossless compression and lossy compression, were applied for image compression, which preserves the image quality. Moreover, some enhancing techniques to increase the quality of a compressed image were employed. These methods were investigated, and several comparison results are demonstrated. Finally, the performance metrics were extracted and analyzed based on state-of-the-art methods. PSNR, MSE, and SSIM are three performance metrics that were used for the sample medical images. Detailed analysis of the measurement metrics demonstrates better efficiency than the other image processing techniques. This study helps to better understand these strategies and assists researchers in selecting a more appropriate technique for a given use case.

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Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network

Saadi

Sarshar

Sadeghi

et al. 2022

Journal of Healthcare Engineering

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One of the leading algorithms and architectures in deep learning is Convolution Neural Network (CNN). It represents a unique method for image processing, object detection, and classification. CNN has shown to be an efficient approach in the machine learning and computer vision fields. CNN is composed of several filters accompanied by nonlinear functions and pooling layers. It enforces limitations on the weights and interconnections of the neural network to create a good structure for processing spatial and temporal distributed data. A CNN can restrain the numbering of free parameters of the network through its weight-sharing property. However, the training of CNNs is a challenging approach. Some optimization techniques have been recently employed to optimize CNN’s weight and biases such as Ant Colony Optimization, Genetic, Harmony Search, and Simulated Annealing. This paper employs the well-known nature-inspired algorithm called Shuffled Frog-Leaping Algorithm (SFLA) for training a classical CNN structure (LeNet-5), which has not been experienced before. The training method is investigated by employing four different datasets. To verify the study, the results are compared with some of the most famous evolutionary trainers: Whale Optimization Algorithm (WO), Bacteria Swarm Foraging Optimization (BFSO), and Ant Colony Optimization (ACO). The outcomes demonstrate that the SFL technique considerably improves the performance of the original LeNet-5 although using this algorithm slightly increases the training computation time. The results also demonstrate that the suggested algorithm presents high accuracy in classification and approximation in its mechanism.

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Metaheuristics Applied to Blood Image Analysis

Cited by 3 publications

References 67 publications

PEMFC model identification using a squeezenet developed by modified transient search optimization algorithm

PEMFC model identification using a squeezenet developed by modified transient search optimization algorithm

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network

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