An Efficient Technique to Segment the Tumor and Abnormality Detection in the Brain MRI Images Using KNN Classifier

Viji, K. S. Angel; Rajesh, D.

doi:10.1016/j.matpr.2020.03.622

Cited by 26 publications

(5 citation statements)

References 16 publications

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“…There are several fuzzy segmentation methods in image processing [9,10]. Among them, fuzzy clustering and methods based on fuzzy rules [11,12] have received more attention in brain MRI segmentation.…”

Section: Fuzzy Methodsmentioning

confidence: 99%

A Systematic Review of Brain MRI Segmentation and Uncertainty Modeling Using Evidence Theory with Implementation of Fuzzy Clustering and Fuzzy Inference Systems Methods

Sedeeq,

Aljobouri,

Hasan

2023

RIA

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Image segmentation, the process of partitioning an image into its constituent parts, is a pivotal step in image processing, particularly with respect to brain MRI images. This operation's complexity is magnified due to inherent uncertainties, which may arise from factors such as noise and intensity non-uniformity. In this study, a systematic review of both non-fuzzy and fuzzy medical image segmentation methods, with a focus on brain MRI images, was undertaken. Practical application of fuzzy clustering and fuzzy inference systems were demonstrated using freely accessible simulated data. This paper presents an emphasis on uncertainty modeling techniques, highlighting the potential of belief structure integration as a significant approach for future hybrid medical image segmentation techniques. The fusion of diverse information sources through this combination is posited to enhance both the accuracy and robustness of uncertainty handling, critical aspects in medical image analysis.

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Section: Fuzzy Methodsmentioning

confidence: 99%

A Systematic Review of Brain MRI Segmentation and Uncertainty Modeling Using Evidence Theory with Implementation of Fuzzy Clustering and Fuzzy Inference Systems Methods

Sedeeq,

Aljobouri,

Hasan

2023

RIA

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“…Viji et al ( 2020 ) proposed a method for classifying brain MRI images using the K-nearest neighbor classifier (KNNC) along with segmentation techniques. They applied preprocessing steps, feature extraction, and a genetic algorithm for optimized feature selection.…”

Section: Literature Reviewmentioning

confidence: 99%

Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling

Rai,

Yoo,

Dashkevych

2024

J Cancer Res Clin Oncol

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Purpose The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-year survival rate as low as 5 to 10%. This underscores the urgent need to improve diagnosis and treatment outcomes through innovative approaches in medical imaging and deep learning techniques. Methods In this work, we propose a novel approach utilizing the two-headed UNetEfficientNets model for simultaneous segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) images. The model combines the strengths of EfficientNets and a modified two-headed Unet model. We utilized a publicly available dataset consisting of 3064 brain MR images classified into three tumor classes: Meningioma, Glioma, and Pituitary. To enhance the training process, we performed 12 types of data augmentation on the training dataset. We evaluated the methodology using six deep learning models, ranging from UNetEfficientNet-B0 to UNetEfficientNet-B5, optimizing the segmentation and classification heads using binary cross entropy (BCE) loss with Dice and BCE with focal loss, respectively. Post-processing techniques such as connected component labeling (CCL) and ensemble models were applied to improve segmentation outcomes. Results The proposed UNetEfficientNet-B4 model achieved outstanding results, with an accuracy of 99.4% after postprocessing. Additionally, it obtained high scores for DICE (94.03%), precision (98.67%), and recall (99.00%) after post-processing. The ensemble technique further improved segmentation performance, with a global DICE score of 95.70% and Jaccard index of 91.20%. Conclusion Our study demonstrates the high efficiency and accuracy of the proposed UNetEfficientNet-B4 model in the automatic and parallel detection and segmentation of brain tumors from MRI images. This approach holds promise for improving diagnosis and treatment planning for patients with brain tumors, potentially leading to better outcomes and prognosis.

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“…The main disadvantage is the lack of visual contrast in medical images. Angel Viji and Hevin Rajesh (2020) presented the texture as well as intensity‐oriented region growth (TIORGW) approach and the K‐nearest neighbour (KNN) were utilized for categorizing a brain MRI image, and the optimization genetic algorithm (GA) was utilized to choose the most suitable texture characteristics from a segmented brain MRI pictures. K‐Nearest neighbour is a straightforward algorithm which holds all obtainable cases and categorizes new information or instances similarity matrix.…”

Section: Related Workmentioning

confidence: 99%

Classification of brain tumours in MRI images using convolutional neural network through Cat Swarm Optimization

Deepak

Sarath

2022

Expert Systems

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In humans, a brain abnormality is a serious illness. Cancer which is the greatest cause of mortality can develop from the tumour. Magnetic resonance imaging (MRI) is among a more extensively utilized medical imaging modalities in brain tumours, then it has become the primary diagnosing mechanism for the treatment and evaluation of brain tumours. Computer‐assisted diagnosis has become a requirement due to the exponential expansion in the quantity of MRIs acquired because of these programmes. Computer‐assisted diagnosis strategies created to increase detection without many systematized readings failed to produce significant improvements in performance measurements. In this regard, the usage of deep learning‐based automatic image processing algorithms appears to be a viable route for identifying brain cancer. In this research, introduce a Cat Swarm Optimization (CSO) algorithm based upon a convolutional neural network (CNN) model utilized to segmentation in a classification of brain tumour. Results of experiments on MRI images using the BRATS dataset show that the CSO algorithm‐CNN model achieved high‐performance in term of 98% of accuracy, precision, specificity, sensitivity and F‐score in the proposed classification task when compared to other classification approaches like support vector machines (SVM) as well as back propagation neural networks (BPNN).

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An Efficient Technique to Segment the Tumor and Abnormality Detection in the Brain MRI Images Using KNN Classifier

Cited by 26 publications

References 16 publications

A Systematic Review of Brain MRI Segmentation and Uncertainty Modeling Using Evidence Theory with Implementation of Fuzzy Clustering and Fuzzy Inference Systems Methods

A Systematic Review of Brain MRI Segmentation and Uncertainty Modeling Using Evidence Theory with Implementation of Fuzzy Clustering and Fuzzy Inference Systems Methods

Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling

Classification of brain tumours in MRI images using convolutional neural network through Cat Swarm Optimization

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