Fractional probabilistic fuzzy clustering and optimization based brain tumor segmentation and classification

Jemimma, T. A.; Vetharaj, Y. Jacob

doi:10.1007/s11042-022-11969-2

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…Moreover, Table 5 demonstrates the analysis of accuracy, sensitivity, and specificity of the proposed IALO with RNN method and exisiting brain tumor classification methods. The sensitivity score obtained by most recent methods like Deep RNN with fractional CSO [22], Whale-Cat Swarm optimization based Deep belief network [13], Deep CNN with Dolphin-SCA [29], and hybrid method proposed by [47] are 95%, 95%, 97.7%, 97.34% respectively. However the sensitivity of proposed method is high 99.23% for BRATS dataset, 99% for J.Cheng dataset CNN with Dolphin-SCA [29], and hybrid method proposed by Bansal and Jindal [47] are 96%, 96%, 95.3%, and 98.82% respectively.…”

Section: Performance Analysis Of Proposed Methodsmentioning

confidence: 99%

“…The proposed method selects the optimum features based on solving the optimization problem by IALO algorithm which yields the higher specificity than the existing method. As compared to existing methods such as, Whale-Cat optimization [13], Hybrid fuzzy brain-storm optimization [23], Deep CNN with Dolphin-SCA [29], Deep RNN with fractional-CSO [22], hybrid method proposed by Bansal and Jindal [47] yield the accuracy as Fig. 16 Convergence curve for J.Cheng dataset using SVM 92.75%, 93.85%, 95.3%, 93.35%, 98.75% and IALO with RNN has a higher accuracy (99.59% for BRATS, 99.18% for J.Cheng dataset).…”

Section: Performance Analysis Of Proposed Methodsmentioning

confidence: 99%

“…In the literature, 15 classification algorithms and 16 feature selection techniques were investigated utilizing their complexity, popularity, and effectiveness. Jemmimmal and Vedtharaj [13] introduced fuzzy-based methodologies to segment the tumor and non-tumor regions of the brain using Fractional Probabilistic FCM (Fr-pFCM [24] provided an automated framework for generating optimal or sub-optimal CNN structures using hybridized elephant herding optimization (EHO). These hybridized metaheuristics framework was used to tune the hyperparameters of CNNs to create a system for instant image classification of glioma brain tumor grades using magnetic resonance imaging.…”

Section: Related Workmentioning

confidence: 99%

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An Improved Ant-Lion Optimization for Deep and Handcrafted Feature Selection in MR Brain Tumor Classification

Saman

Narayanan

2022

Preprint

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Early diagnosis of brain tumors is essential for successful treatment and mortality prevention. The standard method for diagnosing brain tumors is magnetic resonance imaging. Due to the intricate structure of the brain's anatomy, it was difficult to segment and categorize the various types of brain tumors. Moreover, brain MRI contains a significant number of redundant or irrelevant features, classification algorithms struggle to effectively discover patterns in data not having the use of a feature selection algorithm. In this work, we propose a new wrapper feature selection technique for brain tumor classification. Before segmentation, pre-processing is first applied to the input images. The tumor is segmented using active contours, which are driven by laplacian of gaussian energy and optimal region scalable fitting energy. Hand-crafted features using the Histogram of oriented gradient, Gray level co-occurrence matrix, Gray level run length matrix, and deep features using the ResNet50 model are extracted from the segmented brain images. An improved ant lion optimization method (IALO) is proposed for the selection of the best features. Lastly, we classified brain tumors by using Support Vector Machines, Naive Bayes, and Recurrent Neural Networks using two publicly available datasets. The proposed method achieves an accuracy of 99.59% for BRATS and 99.18% for J.cheng brain images respectively. The proposed algorithm's performance was further compared with existing swarm intelligence-based metaheuristic optimization algorithms. From the experimental results, it is observed that the proposed IALO-based RNN method outperforms the existing methods and effectively removes the least significant features while improving classification accuracy.

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Section: Performance Analysis Of Proposed Methodsmentioning

confidence: 99%

Section: Performance Analysis Of Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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An Improved Ant-Lion Optimization for Deep and Handcrafted Feature Selection in MR Brain Tumor Classification

Saman

Narayanan

2022

Preprint

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“…These features are often combined with machine learning algorithms such as Support Vector Machine (SVM), Random Forest, etc. Jemimma et al [20] used fractional probability fuzzy C-means (FRF-PFCM) for automated segmentation of brain glioma MRI data, followed by feature extraction using descriptors, empirical mode decomposition (EMD), local direction pattern (LDP), wavelet transform, etc. The resultant feature set was then input into a deep belief network based on whale cat swarm optimization (WCSO-DBN) for classification.…”

Section: Related Workmentioning

confidence: 99%

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging

Xue,

Lu,

Wang

et al. 2024

PLoS ONE

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In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively.

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“…However, when dealing with noise or outliers in the data, FCM efficacy is only partial. 20 Another crucial factor is that the FCM's results are affected by the starting values of the parameters. In their work on possibilistic CM (PCM) clustering, Ramakrishnan et al and Zhang et al 21,22 suggested a new method called PCM.…”

Section: R E T R a C T E Dmentioning

confidence: 99%

(Retracted) Deep belief network-based image processing for local directional segmentation in brain tumor detection

et al. 2023

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The Editor-in-Chief and the publisher have retracted this article, which was submitted as part of a guest-edited special section. An investigation uncovered evidence of systematic manipulation of the publication process, including compromised peer review. The Editor and publisher no longer have confidence in the results and conclusions of the article. RD, KKH, BPD, and AKV either did not respond directly or could not be reached.

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Fractional probabilistic fuzzy clustering and optimization based brain tumor segmentation and classification

Cited by 9 publications

References 29 publications

An Improved Ant-Lion Optimization for Deep and Handcrafted Feature Selection in MR Brain Tumor Classification

An Improved Ant-Lion Optimization for Deep and Handcrafted Feature Selection in MR Brain Tumor Classification

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging

(Retracted) Deep belief network-based image processing for local directional segmentation in brain tumor detection

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