Hybridized classification approach for magnetic resonance brain images using gray wolf optimizer and support vector machine

Ahmed, Heba A.; Youssef, B; Elkorany, Ahmed S.; Elsharkawy, Zeinab F.; Saleeb, Adel A.; El‐Samie, Fathi E. Abd

doi:10.1007/s11042-019-07876-8

Cited by 13 publications

(5 citation statements)

References 24 publications

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“…The epochs and mapping % are varied in identifying the achievable performance. In the comparative study, the existing GWO-SVM [30], DNN-GAN [21], and MI-ASVD [16] are accounted.…”

Section: Resultsmentioning

confidence: 99%

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Efficient brain tumor detection and classification using magnetic resonance imaging

Sundarasekar¹,

Appathurai²

2021

Biomed. Phys. Eng. Express

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Magnetic Resonance Imaging (MRI) inputs are most noticeable in diagnosing brain tumors via computer and manual clinical understanding. Multi-level detection and classification of the images utilizing computer-aided processing depend on labels and annotations. Though the two processes are dynamic and time-consuming, without which the precise accuracy is less assured. For augmenting the accuracy in processing un-labeled or annotation-less images, this article introduces Absolute Classification-Detection Model (AC-DM). This model uses a conventional neural network for training the morphological variations proficient of achieving label-less classification and tumor detection. The traditional neural network trains the images based on differential lattice morphology for classification and detection. In this process, training for the lattices and their corresponding gradients is validated to improve the precision of the regional analysis. This helps to retain the precision of identifying tumors. The variations are recognized for their lattice mapping in the detected boundaries of the input image. The detected boundaries help to map accurate lattices for adapting morphological transforms. Thus, the partial and complex processing in detecting tumors is restrained in the suggested model, adapting to the classification. The efficiency of the suggested model is verified utilizing accuracy, precision, sensitivity, and classification time.

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“…The epochs and mapping % are varied in identifying the achievable performance. In the comparative study, the existing GWO-SVM [30], DNN-GAN [21], and MI-ASVD [16] are accounted.…”

Section: Resultsmentioning

confidence: 99%

“…For magnetic resonance brain images, Ahmed et al [30] recommended a hybrid classification approach using a gray wolf optimizer and support vector machine. To classify the brain's Magnetic Resonance image as benign and malignant Radial basis function (RBF) kernel is used along with the proposed methods.…”

Section: Related Workmentioning

confidence: 99%

Efficient brain tumor detection and classification using magnetic resonance imaging

Sundarasekar¹,

Appathurai²

2021

Biomed. Phys. Eng. Express

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“…The GWO is introduced different optimization problems in the image processing domain like joint denoising and unmixing of multispectral images [238], camera calibration method [239], multi-level image thresholding [212], multimodal image registration [279], classification of magnetic resonance brain images [280], image segmentation [191], non-blind RGB watermarking [281], sub-pixel displacement measurement [169], and key points selected to simplify the point cloud [282].…”

Section: Applications Of Grey Wolf Optimizermentioning

confidence: 99%

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

et al. 2024

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The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO's appeal lies in its remarkable characteristics: it is parameter-free, derivative-free, conceptually simple, user-friendly, adaptable, flexible, and robust. Its efficacy has been demonstrated across a wide range of optimization problems in diverse domains, including engineering, bioinformatics, biomedical, scheduling and planning, and business. Given the substantial growth and effectiveness of GWO, it is essential to conduct a recent review to provide updated insights. This review delves into the GWO-related research conducted between 2019 and 2022, encompassing over 200 research articles. It explores the growth of GWO in terms of publications, citations, and the domains that leverage its potential. The review thoroughly examines the latest versions of GWO, categorizing them based on their contributions. Additionally, it highlights the primary applications of GWO, with computer science and engineering emerging as the dominant research domains. A critical analysis of the accomplishments and limitations of GWO is presented, offering valuable insights. Finally, the review concludes with a brief summary and outlines potential future developments in GWO theory and applications. Researchers seeking to employ GWO as a problem-solving tool will find this comprehensive review immensely beneficial in advancing their research endeavors.

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“…With this methodology, they achieved an accuracy of 98.33%. The authors in [13] suggest a new method based on gray wolf optimization (GWO) and SVM. By this technique, they obtained 98.75% accuracy.…”

Section: Related Workmentioning

confidence: 99%

A New Distinctive Methodology for the Classification of Brain MR Images Using Histogram Based Local Feature Descriptors

Sowjanya,

Reddy,

Raveena

2023

IJCDS

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Brain tumors can develop at any brain location with uneven boundaries and shapes. Typically, they increased rapidly due to their size doubling in twenty-five days. If they were unrecognized in earlier phases, patients suffered from various medical problems, including death. Therefore, the identification of brain tumors in the earlier stages is one of the critical aspects. In addition, an effective imaging sequence also plays a vital role in tumor diagnosis. Magnetic resonance (MR) imaging is widely used among the available scanning approaches. Therefore, in this article, we develop a distinctive novel method to classify MR-based brain images. Here, initially, we improve the brightness of brain MR images using a median filter, and then we employ image data augmentation to increase the model's accuracy. Later, we obtain the region of interest (ROI) by Otsu's thresholding and morphological operations. Then, we extracted relevant local textures and shaped informative features from the ROI using Enhanced gradient local binary patterns (EGLBPs) and Modified pyramid histogram of oriented gradients (MPHOG). Finally, we perform classification using various supervised learning approaches: support vector machine (SVM), K-nearest neighbors (KNN), and ensemble learning. All these experiments are implemented on Harvard Medical School (HMS) database. Based on the simulation results, our proposed imaging system outperformed state-of-the-art methods in classification and segmentation. Hence, our suggested framework can be used as a predictive tool for diagnosing patients with brain tumors.

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Hybridized classification approach for magnetic resonance brain images using gray wolf optimizer and support vector machine

Cited by 13 publications

References 24 publications

Efficient brain tumor detection and classification using magnetic resonance imaging

Efficient brain tumor detection and classification using magnetic resonance imaging

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

A New Distinctive Methodology for the Classification of Brain MR Images Using Histogram Based Local Feature Descriptors

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