A novel feature selection framework based on grey wolf optimizer for mammogram image analysis

Balasubramaniam, Sathiyabhama; Kumar, Sanjay; Jayanthi, J.; Sathiya, T.; Ilavarasi, A. K.; Yuvarajan, V.; Gopikrishna, K.

doi:10.1007/s00521-021-06099-z

Cited by 57 publications

(32 citation statements)

References 37 publications

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“…Sathiyabhama et al [ 27 ] developed a novel feature selection framework using a Grey Wolf Optimizer (GWO) for analysing the mammogram image. Thus, to derive the appropriate features from the feature set, the present research work introduces a dimensionality reduction algorithm on the basis of GWO-rough set theory.…”

Section: Related Workmentioning

confidence: 99%

“…Experimental result shows that the developed framework obtained better performance in segmentation on DDSM database. Sathiyabhama et al [ 27 ] performed hybridization of GWO and rough set for determining the breast cancer masses using the MIAS dataset which obtained accuracy of 96.4% whereas the developed Versatile Duck Traveler Optimization algorithm by Shankar and Duraisamy [ 28 ] obtained accuracy of 93% for the DDSM dataset and 86.7% of accuracy for the MIAS dataset. The models failed to perform dimensionality reduction without choosing the subsets for describing the attribute size which was failed that lowered the accuracy values.…”

Section: Quantitative Performance On Mias Databasementioning

confidence: 99%

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[Retracted] Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism‐Like Optimization Algorithm

Ittannavar¹,

Havaldar

2022

BioMed Research International

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In recent times, breast mass is the most diagnostic sign for early detection of breast cancer, where the precise segmentation of masses is important to reduce the mortality rate. This research proposes a new multiobjective optimization technique for segmenting the breast masses from the mammographic image. The proposed model includes three phases such as image collection, image denoising, and segmentation. Initially, the mammographic images are collected from two benchmark datasets like Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS). Next, image normalization and Contrast-Limited Adaptive Histogram Equalization (CLAHE) techniques are employed for enhancing the visual capability and contrast of the mammographic images. After image denoising, electromagnetism-like (EML) optimization technique is used for segmenting the noncancer and cancer portions from the mammogram image. The proposed EML technique includes the advantages like enhanced robustness to hold the image details and adaptive to local context. Lastly, template matching is carried out after segmentation to detect the cancer regions, and then, the effectiveness of the proposed model is analysed in light of Jaccard coefficient, dice coefficient, specificity, sensitivity, and accuracy. Hence, the proposed model averagely achieved 92.3% of sensitivity, 99.21% of specificity, and 98.68% of accuracy on DDSM dataset, and the proposed model averagely achieved 92.11% of sensitivity, 99.45% of specificity, and 98.93% of accuracy on MIAS dataset.

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Section: Related Workmentioning

confidence: 99%

Section: Quantitative Performance On Mias Databasementioning

confidence: 99%

[Retracted] Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism‐Like Optimization Algorithm

Ittannavar¹,

Havaldar

2022

BioMed Research International

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“…Maurya et al [16] Cuckoo search (CS) algorithm CSA is used to balance the contrast and brightness Nickfarjam et al [17] Modified PSO algorithm Consists of the standard deviation and edge content Sathiyabhama, B et al [18] Gray wolf optimizer algorithm Improve with rough set theory Qin et al X [19] Modified PSO algorithm A modified inertia weight function used in the PSO Acharya et al [20] Modified genetic (GA) algorithm Adaptive histogram equalization technique used in the GA Muniyappan et al [21] Adaptive genetic algorithm Introduce adaptive crossover and mutation operations in GA Bhandari et al [22] CS algorithm Improve the contrast of low-contrast image using CSA Kamoona et al [23] Modified CS algorithm Image transform enhancement functions and objective function Prasath et al [24] Modified CS algorithm Distance-Oriented Cuckoo Search (DOCS) algorithm Sridevi et al [25] Modified genetic algorithm Fractional Genetic Algorithm Chen et al [26] Artificial bee colony algorithm A new fitness function and new image transformation function Banharnsakun et al [27] Artificial bee colony algorithm Image edge detection enhancement using ABC algorithm…”

Section: Authors Algorithms Strategymentioning

confidence: 99%

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Xiong

Zhang

et al. 2021

Mathematics

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At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

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“…In [34], the authors presented FS approach, called GWORS, using a combination between grey wolf optimizer (GWO) and rough set for mammogram image analysis. The GWORS was compared to well-known FS methods, and it obtained competitive performance.…”

Section: Related Workmentioning

confidence: 99%

An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection

Ibrahim

Abualigah

Ewees

et al. 2021

Entropy

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With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively.

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A novel feature selection framework based on grey wolf optimizer for mammogram image analysis

Cited by 57 publications

References 37 publications

[Retracted] Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism‐Like Optimization Algorithm

[Retracted] Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism‐Like Optimization Algorithm

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection

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