Brain Tumor Segmentation in 3D-MRI Based on Artificial Bee Colony and Level Set

Ibrahim, Yasmine Mahmoud; Darwish, Saad M.; Sheta, Walaa M.

doi:10.1007/978-3-030-44289-7_19

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…These types of optimization methods [17] utilize the concept of a nature-inspired optimization framework. Any ABC-based optimization technique is formulated by using food sources.…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

Hyperparameter optimization for deep neural network models: a comprehensive study on methods and techniques

Roy,

Mehera,

Pal

et al. 2023

Innovations Syst Softw Eng

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Advancements in computing and storage technologies have significantly contributed to the adoption of deep learning (DL)-based models among machine learning (ML) experts. Although a generic model can be used in the search for a near-optimal solution in any problem domain, what makes these DL models context-sensitive is the combination of the training data and the hyperparameters. Due to the lack of inherent explainability of DL models the Hyperparameter Optimization (HPO) or tuning specific to each model is a combination of art, science, and experience. In this article, we have explored various existing methods or ways to identify the optimal set of values for the hyperparameters specific to the DL models along with the techniques to realize those methods in real-life situations. The article also includes a detailed comparative study among various state-of-the-art HPO techniques using the Keras Tuner tuning toolkit and highlights the observations describing how the model performance can be improved by applying various HPO techniques.

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“…These types of optimization methods [17] utilize the concept of a nature-inspired optimization framework. Any ABC-based optimization technique is formulated by using food sources.…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

Hyperparameter optimization for deep neural network models: a comprehensive study on methods and techniques

Roy,

Mehera,

Pal

et al. 2023

Innovations Syst Softw Eng

View full text Add to dashboard Cite

show abstract

“…Since each cluster is evolved solely, after a number of iterations, the population is re-clustered with the aim of information exchange among the clusters. Ibrahim et al (2020) presented a modified ABC clustering technique for fast and accurate level set segmentation to extract the tumour. In ABC, instead of random initialization, the food source positions are generated using K-Means.…”

Section: Using Clustering Techniques In Abcmentioning

confidence: 99%

A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

2020

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“…Clustering has been used mostly in different ways in the initialization phase of the ABC algorithms. K-means clustering was either used to add diversity to the population or to detect the clusters in the optima [18], [31], [32]. Reinforcement learning has also been used in several studies to improve the searching process of the ABC [33], [34].…”

Section: Studies Employing Learning Techniques On Abcmentioning

confidence: 99%

OptABC: an Optimal Hyperparameter Tuning Approach for Machine Learning Algorithms

Zahedi,

Mohammadi,

Amini

2021

Preprint

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Hyperparameter tuning in machine learning algorithms is a computationally challenging task due to the large-scale nature of the problem. In order to develop an efficient strategy for hyper-parameter tuning, one promising solution is to use swarm intelligence algorithms. Artificial Bee Colony (ABC) optimization lends itself as a promising and efficient optimization algorithm for this purpose. However, in some cases, ABC can suffer from a slow convergence rate or execution time due to the poor initial population of solutions and expensive objective functions. To address these concerns, a novel algorithm, OptABC, is proposed to help ABC algorithm in faster convergence toward a near-optimum solution. OptABC integrates artificial bee colony algorithm, K-Means clustering, greedy algorithm, and opposition-based learning strategy for tuning the hyper-parameters of different machine learning models. OptABC employs these techniques in an attempt to diversify the initial population, and hence enhance the convergence ability without significantly decreasing the accuracy. In order to validate the performance of the proposed method, we compare the results with previous state-of-the-art approaches. Experimental results demonstrate the effectiveness of the OptABC compared to existing approaches in the literature.

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Brain Tumor Segmentation in 3D-MRI Based on Artificial Bee Colony and Level Set

Cited by 5 publications

References 23 publications

Hyperparameter optimization for deep neural network models: a comprehensive study on methods and techniques

Hyperparameter optimization for deep neural network models: a comprehensive study on methods and techniques

A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

OptABC: an Optimal Hyperparameter Tuning Approach for Machine Learning Algorithms

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