Review on Brain Tumor Segmentation and Classification Techniques

Zulpe, Nitish; Pawar, Vivek

doi:10.17577/ijertv6is110008

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Among these imaging techniques, Due to its advantages such as its excellent soft-tissue contrast, lack of ionising radiation, and non-invasive nature, MRI has been frequently employed by researchers and radiologists to detect brain tumors.The traditional brain tumor detection approach, which assists doctors in examining MRI scans, is not only time-consuming but also has a high error rate. Furthermore, the final results of traditional systems rely primarily on biopsy, and it is an invasive procedure that can cause inflammation and pain to patients [3] To decrease the labor needs and improve the patient's survival rate, it is necessary to bring up automated systems for brain tumor detection. This system usually contains five major processes including pre-processing, tumor segmentation, feature extraction, feature selection, and classification.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

Saman

Narayanan

2022

Preprint

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“…Harshvardhan et al[13] recently proposed a logistic regression classifier that uses GLCM-based features as predictors for early detection and recognition of glaucoma by ocular thermal images. Zulpe and Pawar[40] used artificial neural network techniques on GLCM texture features to classify brain tumors from MR images. More recently, Singh et al[32] explored GLCM feature extraction on 330 mammograms concluding that random forest yielded the best classification performance in the breast cancer study.…”

Section: Introductionmentioning

confidence: 99%

Spatial Bayesian modeling of GLCM with application to malignant lesion characterization

Xiao

Guindani

et al. 2018

Journal of Applied Statistics

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The emerging field of cancer radiomics endeavors to characterize intrinsic patterns of tumor phenotypes and surrogate markers of response by transforming medical images into objects that yield quantifiable summary statistics to which regression and machine learning algorithms may be applied for statistical interrogation. Recent literature has identified clinicopathological association based on textural features deriving from gray-level co-occurrence matrices (GLCM) which facilitate evaluations of gray-level spatial dependence within a delineated region of interest. GLCM-derived features, however, tend to contribute highly redundant information. Moreover, when reporting selected feature sets, investigators often fail to adjust for multiplicities and commonly fail to convey the predictive power of their findings. This article presents a Bayesian probabilistic modeling framework for the GLCM as a multivariate object as well as describes its application within a cancer detection context based on computed tomography. The methodology, which circumvents processing steps and avoids evaluations of reductive and highly correlated feature sets, uses latent Gaussian Markov random field structure to characterize spatial dependencies among GLCM cells and facilitates classification via predictive probability. Correctly predicting the underlying pathology of 81% of the adrenal lesions in our case study, the proposed method outperformed current practices which achieved a maximum accuracy of only 59%. Simulations and theory are presented to further elucidate this comparison as well as ascertain the utility of applying multivariate Gaussian spatial processes to GLCM objects.

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Role of deep learning in brain tumor detection and classification (2015 to 2020): A review

Nazir

Shakil

Khurshid

2021

Computerized Medical Imaging and Graphics

157

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Review on Brain Tumor Segmentation and Classification Techniques

Cited by 3 publications

References 16 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

Spatial Bayesian modeling of GLCM with application to malignant lesion characterization

Role of deep learning in brain tumor detection and classification (2015 to 2020): A review

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