An improved ensemble pruning for mammogram classification using modified Bees algorithm

Qasem, Ashwaq; Abdullah, Siti Norul Huda Sheikh; Sahran, Shahnorbanun; Albashish, Dheeb; Goudarzi, Shidrokh; Arasaratnam, Shantini

doi:10.1007/s00521-022-06995-y

Cited by 17 publications

(3 citation statements)

References 49 publications

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“…Optimization-based ensemble pruning techniques rely on defining the objective function and the solution space search strategy. In the last three years, various bioinspired algorithms, such as artificial fish swarm algorithm [47], bee algorithm [48], and salp swarm algorithm [49], have been applied to pruning problems and achieved good results.…”

Section: Optimize Ensemble Systemmentioning

confidence: 99%

Multi-Strategy Improved Northern Goshawk Optimization Algorithm and Application

Zhang

2024

IEEE Access

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The Northern Goshawk Optimization Algorithm (NGO) is a population-based meta-heuristic algorithm inspired by the hunting behavior of the northern goshawk. Compared with other algorithms, NGO have a certain competitiveness, but there is still an imbalance in development and exploration, and it is easy to fall into the local optimal. This paper proposes an improved multi-strategy Improved Northern Goshawk optimization algorithm (INGO) to address these shortcomings. INGO uses an improved tent chaos mapping strategy to generate the initial population and improve the quality of the initial solution set. The levy flight strategy is introduced in the hunting stage of the NGO to improve the search range of the solution and avoid the algorithm's prematurity. In addition, we introduce a nonlinear convergence factor and heart-shaped search strategy to reduce the probability of the algorithm falling into the local optimal and improve the convergence speed of the algorithm. We evaluated INGO's performance using 23 benchmark functions, CEC2017 test suite functions, and three constrained engineering optimization problems and validated its optimization using Wilcoxon rank sum tests. Experimental results show that the algorithm has higher convergence accuracy and better detection ability. Finally, INGO was applied to optimize the ensemble learning system.INDEX TERMS Northern Goshawk Optimization Algorithm; metaheuristics; levy flight; ensemble learning;CEC2017.

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Section: Optimize Ensemble Systemmentioning

confidence: 99%

Multi-Strategy Improved Northern Goshawk Optimization Algorithm and Application

Zhang

2024

IEEE Access

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“…Despite their extensive use, a single CNN model has the restricted power to capture discriminative features from colon histopathology images, resulting in unsatisfactory classification accuracy ( Yang et al, 2019 ). Thus, merging a group of weak learners forms an ensemble learning model, which is likely to be a strong learner and moderate the shortcomings of the weak learners ( Qasem et al, 2022 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images

Albashish

2022

PeerJ Computer Science

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Deep convolutional neural networks (CNN) manifest the potential for computer-aided diagnosis systems (CADs) by learning features directly from images rather than using traditional feature extraction methods. Nevertheless, due to the limited sample sizes and heterogeneity in tumor presentation in medical images, CNN models suffer from training issues, including training from scratch, which leads to overfitting. Alternatively, a pre-trained neural network’s transfer learning (TL) is used to derive tumor knowledge from medical image datasets using CNN that were designed for non-medical activations, alleviating the need for large datasets. This study proposes two ensemble learning techniques: E-CNN (product rule) and E-CNN (majority voting). These techniques are based on the adaptation of the pretrained CNN models to classify colon cancer histopathology images into various classes. In these ensembles, the individuals are, initially, constructed by adapting pretrained DenseNet121, MobileNetV2, InceptionV3, and VGG16 models. The adaptation of these models is based on a block-wise fine-tuning policy, in which a set of dense and dropout layers of these pretrained models is joined to explore the variation in the histology images. Then, the models’ decisions are fused via product rule and majority voting aggregation methods. The proposed model was validated against the standard pretrained models and the most recent works on two publicly available benchmark colon histopathological image datasets: Stoean (357 images) and Kather colorectal histology (5,000 images). The results were 97.20% and 91.28% accurate, respectively. The achieved results outperformed the state-of-the-art studies and confirmed that the proposed E-CNNs could be extended to be used in various medical image applications.

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“…It can be safely assumed that the two chosen healthcare institutions for this study, University Malaya Medical Center (UMMC) and Hospital Kuala Lumpur (HKL), are adequate representations of healthcare facilities (Azlan et al, 2020;Qasem et al, 2022). UMMC strives to achieve the highest quality of standards not only in Malaysia but throughout the world (UMMC, 2022).…”

Section: Data Collection Procedures and Samplementioning

confidence: 99%

Determinants of effectiveness of lean healthcare performance in Malaysian public hospitals

Ong

Zailani

Kanapathy

et al. 2022

TQM

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PurposeThis study aims to explore the lean leadership factors influencing the effectiveness of lean healthcare performance at hospitals.Design/methodology/approachA total of 217 healthcare professionals’ responses were included for data analysis. The data were analyzed using the partial least square technique.FindingsThe findings revealed that improvement culture, gemba (hands-on approach), hoshin kanri (goal-oriented) and qualification are the crucial lean leadership factors that positively influence the effectiveness of lean healthcare performance at hospitals. The results indicated that the self-development of lean leaders has no significant impact on the effectiveness of lead healthcare performance.Originality/valueThe findings of this study could contribute to the healthcare institutions in Malaysia perceive the benefits of lean healthcare and show a positive intention of using lean leadership principles.

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An improved ensemble pruning for mammogram classification using modified Bees algorithm

Cited by 17 publications

References 49 publications

Multi-Strategy Improved Northern Goshawk Optimization Algorithm and Application

Multi-Strategy Improved Northern Goshawk Optimization Algorithm and Application

Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images

Determinants of effectiveness of lean healthcare performance in Malaysian public hospitals

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