Smart Identification of Topographically Variant Anomalies in Brain Magnetic Resonance Imaging Using a Fish School-Based Fuzzy Clustering Approach

Alagarsamy, Saravanan; Zhang, Yudong; Govindaraj, Vishnuvarthanan; Rajasekaran, M. Pallikonda; Sankaran, Sindhuja

doi:10.1109/tfuzz.2020.3015591

Cited by 30 publications

(8 citation statements)

References 55 publications

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“…Anisotropic diffusion filtering based on partial differential equations, that is, the PM (Perona and Malik) model, can remove the noises in the image while retaining or even enhancing the edge information. Concepts of mass diffusion theory and iterative smoothing are introduced into image processing (Zhang et al, 2020). The PM model is described as:…”

Section: Segmentation Performance Of Fuzzy System For Brain Imagesmentioning

confidence: 99%

Fuzzy System Based Medical Image Processing for Brain Disease Prediction

et al. 2021

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The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. A brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation) to ensure the model safety performance. Brain MRI images collected from a Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrate that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 s on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under DSC (Dice Similarity Coefficient), reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. In a word, the proposed algorithm can provide higher accuracy, a more apparent denoising effect, and the best segmentation and recognition effect than other models while ensuring energy consumption. The results can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.

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Section: Segmentation Performance Of Fuzzy System For Brain Imagesmentioning

confidence: 99%

Fuzzy System Based Medical Image Processing for Brain Disease Prediction

et al. 2021

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“…The best selection of features and classification stages is achieved using an updated model of the whale optimization method based on chaos theory and logistic mapping approach. Furthermore, Alagarsamy et al (2021) [ 11 ] used a spatially constrained fish school optimization method (SCFSO) and an interval type-II vague logic system to address brain tumour abnormalities. SCFSO and IT2FLS can intervene and investigate large datasets and complicated cancers.…”

Section: Related Workmentioning

confidence: 99%

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation

Kalpana

Bennet

Rahmani³

2022

BioMed Research International

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Brain tumor has the foremost distinguished etiology of high morality. Neoplasm, a categorization of brain tumors, is very operative in distinguishing and determining the tumor’s exact location in the brain. Magnetic resonance imaging (MRI) is an efficient noninvasive technique for the anatomical examination of brain tumors. Growth tissues have a distinguishable look in MRI pictures in order that they are unit-wide used for brain tumor feature extraction. The existing research algorithms for brain tumors have some limitations such as different qualities, low sensitivity, and diagnosing the tumor at its stages. In this particular piece of research, an innovative method of optimization known as the procedure for lightning attachment algorithm (PLA) is used, and for the purpose of classification, a CNN model known as DenseNet-169 is applied. PLA was used in order to optimize the growth, and a network model known as the DenseNet-169 model was utilized in order to extract the various growth-optimization choices. First, the MR images of the brain were preprocessed to remove any outliers. Next, the Dense Net-169 CNN model was used to extract network choices from the MR images. In addition, it is used to execute the function of a classifier in order to identify the growth as either an aberrant growth or a traditional growth. In addition, the publicly benchmarked datasets that are widely utilized have validated the algorithmic rule that was granted. The planned system demonstrates the satisfactory accuracy in getting ready to on the dataset and outperforms many of the notable current techniques.

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“…These issues have increased as a result of errors generated by the operator, device/instruments, and environment, all of which may be addressed using the innovative segmentation approach presented in this study. To correct the inaccurate predictions of anomalies in different topographical locations in MRI brain subjects, in [1] a new method combined the functions of the Interval Type-II Fuzzy Logic System (IT2FLS) and Spatially Constrained Fish School Optimization algorithm (SCFSO). This technique can readily interfere with and analyze huge datasets and complicated tumors (anomalies), and it may be the proactive estimation for being incorporated or implemented in clinical experience for the benefit of both physicians and the patients, and it may offer doctors with a meaningful experience.…”

Section: Related Workmentioning

confidence: 99%

“…These results compared with existing approaches, and this method may be used to segment T1-weighted (T1-W), Flair (Fluid Attenuated Inversion Recovery), and T2weighted (T2-W) MRI sequences of varying axis coordination. This approach allows for the separation of non-tumor (edema) and tumor areas, and such an advantage is the therapeutic pre-planning can be frequently done [1].…”

Section: Related Workmentioning

confidence: 99%

A Study on Segmenting Brain Tumor MRI Images

N¹

2021

J Comput Sci Intell Technol

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Segmentation of image has traditionally been referred to as the initial stage in image processing. A successful segmentation output will make image processing analysis considerably further easier. There are several image segmentation techniques and methodologies available. Clustering is the most widely used segmentation algorithm for image processing. Segmentation of tumor using magnetic resonance imaging (MRI) data is a critical procedure yet time-consuming process manually carried out by medical specialists. Due to the considerable difference in the tumor tissue appearances across patients, as well as their occasionally similar resemblance to normal tissues, automating this procedure is difficult. MRI is a sort of sophisticated medical imaging that offers precise information on the human soft tissues. To identify and segment the brain tumor using MRI images, several brain tumor segmentation and detection approaches are analyzed. The benefits and drawbacks of these approaches for brain tumor identification and segmentation are analyzed, with an emphasis on illuminating the benefits and limitations of these techniques for brain tumor segmentation and detection. The MRI image usage in segmentation and detection on various processes is also covered. This analysis provides an overview of several segmentation methods for identifying brain tumors from MRI images of the brain, as well as the usage of various Clustering Techniques.

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Smart Identification of Topographically Variant Anomalies in Brain Magnetic Resonance Imaging Using a Fish School-Based Fuzzy Clustering Approach

Cited by 30 publications

References 55 publications

Fuzzy System Based Medical Image Processing for Brain Disease Prediction

Fuzzy System Based Medical Image Processing for Brain Disease Prediction

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation

A Study on Segmenting Brain Tumor MRI Images

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