Brain tumor segmentation using Cuckoo Search optimization for Magnetic Resonance Images

George, Elsa; Rosline, G. Jeba; Rajesh, D. Gnana

doi:10.1109/ieeegcc.2015.7060024

Cited by 15 publications

(9 citation statements)

References 13 publications

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“…Cuckoo search is flawless, similar to the cuckoo's breeding behavior, and can be used to solve a variety of optimization problems. It is based on the idealized rules that each cuckoo lays one egg at a time and deposits it in a nest selected at random [52].…”

Section: Cuckoo Searchmentioning

confidence: 99%

Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

Kaur

Singh

Mansoor

et al. 2022

Wireless Communications and Mobile Computing

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The brain tumor is the 22nd most common cancer worldwide, with 1.8% of new cancers. It is likely the most severe ailment that necessitates early discovery and treatment, and it requires the competence of neurosubject-matter experts and radiologists. Because of their enormous increases in data search and extraction speed and accuracy, as well as individualized treatment suggestions, machine and deep learning techniques are being increasingly commonly applied throughout healthcare industries. The current study depicts the methodologies and procedures used to detect a tumor inside the brain utilizing machine and deep learning techniques. Initially, data were preprocessed using contrast limited adaptive histogram equalization. Then, features were extracted using principal component analysis and independent component analysis (ICA). Next, the image was smoothed using multiple optimization techniques such as firefly and cuckoo search, lion, and bat optimization. Finally, Naïve Bayes and recurrent neural networks were utilized to classify the improved results. According to the findings, the ICA with cuckoo search and Naïve Bayes has the best mean square error rate of 1.02. With 64.81% peak signal-to-noise and 98.61% accuracy, ICA with hybrid optimization and a recurrent neural network (RNN) proved to better than the other algorithms. Furthermore, a Smartphone application is designed to perform quick and decisive actions. It helps neurologists and patients identify the tumor from a brain image in the early stages.

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Section: Cuckoo Searchmentioning

confidence: 99%

Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

Kaur

Singh

Mansoor

et al. 2022

Wireless Communications and Mobile Computing

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“…Several techniques for brain tumor segmentation based on metaheuristics have been reported in the literature [31−32]. In [31], the Cuckoo search algorithm, an efficient optimization model, was applied to brain tumor segmentation from MRI images. Ant colony optimization metaheuristic and a fuzzy classification approach were combined in [32] to segment and extract the suspicious region from the brain MRI image containing the tumor position.…”

Section: Brain Tumor Segmentation Algorithmsmentioning

confidence: 99%

Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation

Atia¹,

Benzaoui²,

Jacques³

et al. 2022

Preprint

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Segmentation of brain tumor images is a major research topic in medical imaging to have a refined detection and understanding of abnormal masses in the brain. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block to classify it as tumor or not. A thorough evaluation of the proposed algorithm is performed using the MRI database provided by the Kouba imaging center in Algiers, Algeria. Estimates of the selected fitness function are first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion and demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm is then compared to the results of several state-of-the-art techniques, including fuzzy C-means, K-means, Otsu thresholding, local thresholding, and watershed segmentation. The results obtained using Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements demonstrate the superiority of the proposed optimized segmentation algorithm over equivalent techniques.

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“…The machine confirmed the promising consequences for detection and segmentation of tumor. George et al [7] had labored at the simple concepts of cuckoo search set of rules for segmentation of mind tumor from the Magnetic Resonance Images (MRI). They had advanced a diagnostic gadget the usage of the first-rate optimization method known as the cuckoo search, which could assist the radiologist to have a 2d opinion.…”

Section: A Brain Tumor Detection Algorithmsmentioning

confidence: 99%

Diagnosis and Classification of Brain Tumor

Singh*,

Prakash

2019

IJEAT

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The aim of this paper is to provide an outline on cerebrum (brain) tumor diagnoses for folks that are new to virtual medical image processing. The strange improvement of cells within the brain is brain Tumor. Detection of brain tumor at early stage is feasible with image processing and the development of device learning. For early detection of extraordinary adjustments in tumor cells, Computer tomography, Magnetic resonance imaging strategies are used. Early detection and identification of tumor is the handiest manner to get remedy. Brain tumor is classed into two kinds benign and malignant tumor. Various imaging processing strategies had been proposed in latest couple of years for detection and class of brain tumor. Automatic segmentation method the usage of clustering and convolution neural community gives nice consequences. The PCA has been used for reducing the features from the segmented area gives superb outcomes compared to other techniques. For classification of brain tumors diverse algorithms any such Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor are reviewed. These strategies correctly paintings on CT and MRI images

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Brain tumor segmentation using Cuckoo Search optimization for Magnetic Resonance Images

Cited by 15 publications

References 13 publications

Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation

Diagnosis and Classification of Brain Tumor

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