Automated Detection and Classification of Meningioma Tumor from MR Images Using Sea Lion Optimization and Deep Learning Models

Meningiomas are the most prevalent benign intracranial life-threatening brain tumors, with a life expectancy of a few months in the later stages, so this type of tumor in the brain image should be recognized and detected efficiently. The source of meningiomas is unknown. Radiation exposure, particularly during childhood, is the sole recognized environmental risk factor for meningiomas. The imaging technique of magnetic resonance imaging (MRI) is commonly used to detect most tumor forms as it is a non-invasive and painless method. This study introduces a CNN-HHO integrated automated identification model, which makes use of SeaLion optimization methods for improving overall network optimization. In addition to these techniques, various CNN models such as Resnet, VGG, and DenseNet have been utilized to give an overall influence of CNN with SeaLion in each methodology. Each model is tested on our benchmark dataset for accuracy, specificity, dice coefficient, MCC, and sensitivity, with DenseNet outperforming the other models with a precision of 98%. The proposed methods outperform existing alternatives in the detection of brain tumors, according to the existing experimental findings.

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A Review on Some Classification Methods of Brain Tumor MRI Datasets

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2062114 ◽

2020 ◽

pp. 421-424

Author(s):

Nirmal Mungale ◽

Snehal Kene ◽

Amol Chaudhary

Keyword(s):

Brain Tumor ◽

Brain Mri ◽

Mr Images ◽

Classification Techniques ◽

Abnormal Growth ◽

Life Threatening ◽

Magnetic Resonance Imaging Mri ◽

Recent Classification ◽

The Brain

Brain tumor is a life-threatening disease. Brain tumor is formed by the abnormal growth of cells inside and around the brain. Identification of the size and type of tumor is necessary for deciding the course of treatment of the patient. Magnetic Resonance Imaging (MRI) is one of the methods for detection of tumor in the brain. The classification of MR Images is a difficult task due to variety and complexity of brain tumors. Various classification techniques have been identified for brain MRI tumor images. This paper reviews some of these recent classification techniques.

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Performance Analysis of Deep Belief Neural Network for Brain Tumor Classification

Journal of Computational Science and Intelligent Technologies ◽

10.53409/mnaa.jcsit20201305 ◽

2020 ◽

Vol 1 (3) ◽

pp. 29-34

Author(s):

Sreenivas Eeshwaroju ◽

◽

Praveena Jakula ◽

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Imaging Techniques ◽

Structural Heterogeneity ◽

Low Complexity ◽

Automated Classification ◽

Tumor Identification ◽

Magnetic Resonance Imaging Mri ◽

The Brain

The brain tumors are by far the most severe and violent disease, contributing to the highest degree of a very low life expectancy. Therefore, recovery preparation is a crucial step in improving patient quality of life. In general , different imaging techniques such as computed tomography ( CT), magnetic resonance imaging ( MRI) and ultrasound imaging have been used to examine the tumor in the brain, lung , liver, breast , prostate ... etc. MRI images are especially used in this research to diagnose tumor within the brain with classification results. The massive amount of data produced by the MRI scan, therefore, destroys the manual classification of tumor vs. non-tumor in a given period. However for a limited number of images, it is presented with some constraint that is precise quantitative measurements. Consequently, a trustworthy and automated classification scheme is important for preventing human death rates. The automatic classification of brain tumors is a very challenging task in broad spatial and structural heterogeneity of the surrounding brain tumor area. Automatic brain tumor identification is suggested in this research by the use of the classification with Deep Belief Network (DBN). Experimental results show that the DBN archive rate with low complexity seems to be 97 % accurate compared to all other state of the art methods.

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A Review on Brain Tumor Classification Methodologies

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst20717 ◽

2019 ◽

pp. 346-359 ◽

Cited By ~ 1

Author(s):

Bichitra Panda ◽

Chandra Sekhar Panda

Keyword(s):

Feature Extraction ◽

Brain Tumor ◽

Brain Tumors ◽

Brain Mri ◽

Tumor Classification ◽

Low Grade ◽

High Grade ◽

Mr Images ◽

The Brain

Brain tumor is one of the leading disease in the world. So automated identification and classification of tumors are important for diagnosis. Magnetic resonance imaging (MRI)is widely used modality for imaging brain. Brain tumor classification refers to classify the brain MR images as normal or abnormal, benign or malignant, low grade or high grade or types. This paper reviews various techniques used for the classification of brain tumors from MR images. Brain tumor classification can be divided into three phases as preprocessing, feature extraction and classification. As segmentation is not mandatory for classification, hence resides in the first phase. The feature extraction phase also contains feature reduction. DWT is efficient for both preprocessing and feature extraction. Texture analysis based on GLCM gives better features for classification where PCA reduces the feature vector maintaining the accuracy of classification of brain MRI. Shape features are important where segmentation has already been performed. The use of SVM along with appropriate kernel techniques can help in classifying the brain tumors from MRI. High accuracy has been achieved to classify brain MRI as normal or abnormal, benign or malignant and low grade or high grade. But classifying the tumors into more particular types is more challenging.

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Detection and Classification of Brain Tumor in MRI Images Using Wavelet Transform and Convolutional Neural Network

Journal of Advances in Medicine and Medical Research ◽

10.9734/jammr/2020/v32i1230539 ◽

2020 ◽

pp. 15-26

Author(s):

Ahmad M. Sarhan

Keyword(s):

Brain Tumor ◽

Wavelet Transform ◽

Brain Tumors ◽

Brain Mri ◽

Discrete Wavelet ◽

Abnormal Cells ◽

Magnetic Resonance Imaging Mri ◽

The Brain ◽

Mri Image

A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign or malignant. Conventional diagnosis of a brain tumor by the radiologist, is done by examining a set of images produced by magnetic resonance imaging (MRI). Many computer-aided detection (CAD) systems have been developed in order to help the radiologist reach his goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents a novel CAD technique for the classification of brain tumors in MRI images The proposed system extracts features from the brain MRI images by utilizing the strong energy compactness property exhibited by the Discrete Wavelet transform (DWT). The Wavelet features are then applied to a CNN to classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 98.5%.

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Beyond Oncological Hyperthermia: Physically Drivable Magnetic Nanobubbles as Novel Multipurpose Theranostic Carriers in the Central Nervous System

Molecules ◽

10.3390/molecules25092104 ◽

2020 ◽

Vol 25 (9) ◽

pp. 2104 ◽

Cited By ~ 1

Author(s):

Eleonora Ficiarà ◽

Shoeb Anwar Ansari ◽

Monica Argenziano ◽

Luigi Cangemi ◽

Chiara Monge ◽

...

Keyword(s):

Central Nervous System ◽

Nervous System ◽

Brain Tumors ◽

Ad Hoc ◽

Superparamagnetic Iron Oxide ◽

Conventional Radiotherapy ◽

The Central Nervous System ◽

Magnetic Resonance Imaging Mri ◽

The Brain

Magnetic Oxygen-Loaded Nanobubbles (MOLNBs), manufactured by adding Superparamagnetic Iron Oxide Nanoparticles (SPIONs) on the surface of polymeric nanobubbles, are investigated as theranostic carriers for delivering oxygen and chemotherapy to brain tumors. Physicochemical and cyto-toxicological properties and in vitro internalization by human brain microvascular endothelial cells as well as the motion of MOLNBs in a static magnetic field were investigated. MOLNBs are safe oxygen-loaded vectors able to overcome the brain membranes and drivable through the Central Nervous System (CNS) to deliver their cargoes to specific sites of interest. In addition, MOLNBs are monitorable either via Magnetic Resonance Imaging (MRI) or Ultrasound (US) sonography. MOLNBs can find application in targeting brain tumors since they can enhance conventional radiotherapy and deliver chemotherapy being driven by ad hoc tailored magnetic fields under MRI and/or US monitoring.

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An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

Complex & Intelligent Systems ◽

10.1007/s40747-021-00310-3 ◽

2021 ◽

Author(s):

Muhammad Irfan Sharif ◽

Jian Ping Li ◽

Javeria Amin ◽

Abida Sharif

Keyword(s):

Brain Tumor ◽

Tumor Detection ◽

Entropy Method ◽

Feature Extraction And Selection ◽

Tumor Region ◽

Selection For ◽

Magnetic Resonance Imaging Mri ◽

The Brain ◽

Complicated Task

AbstractBrain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed toMcCulloch'sKapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

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Classification of Brain MR Images Using Corpus Callosum Shape Measurements

Medical Imaging ◽

10.4018/978-1-5225-0571-6.ch060 ◽

2017 ◽

pp. 1427-1436

Author(s):

Gaurav Vivek Bhalerao ◽

Niranjana Sampathila

Keyword(s):

Neural Network ◽

Corpus Callosum ◽

Clustering Algorithm ◽

Neurological Diseases ◽

Back Propagation ◽

Mr Images ◽

Dementia Patients ◽

Male Patients ◽

The Brain

The corpus callosum is the largest white matter structure in the brain, which connects the two cerebral hemispheres and facilitates the inter-hemispheric communication. Abnormal anatomy of corpus callosum has been revealed for various brain related diseases. Being an important biomarker, Magnetic Resonance Imaging of the brain followed by corpus callosum segmentation and feature extraction has found to be important for the diagnosis of many neurological diseases. This paper focuses on classification of T1-weighted mid-sagittal MR images of brain for dementia patients. The corpus callosum is segmented using K-means clustering algorithm and corresponding shape based measurements are used as features. Based on these shape based measurements, a back-propagation neural network is trained separately for male and female dataset. The input data consists of 54 female and 31 male patients. This paper reports classification accuracy up to 92% for female patients and 94% for male patients using neural network classifier.

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Audit of histomorphology and immunohistochemistry of the brain tumors: Revisited in context to the WHO 2016 molecular classification

International Journal of Molecular and Immuno Oncology ◽

10.18203/issn.2456-3994.intjmolimmunooncol20170054 ◽

2017 ◽

Vol 2 (1) ◽

pp. 15

Author(s):

Tanushri Mukherjee ◽

Rajat Dutta ◽

Joydeep Ghosh

Keyword(s):

Social Sciences ◽

Statistical Analysis ◽

Brain Tumors ◽

Molecular Classification ◽

Histopathological Diagnosis ◽

Treatment Change ◽

Tumor Patients ◽

The Brain

Background: The WHO 2016 molecular classification corroborating with the histology has given more significant diagnostic objectivity to the diagnosis of brain tumors and it is more reliable for instituting therapy as the heterogeneity and observer subjectivity are bypassed with the addition of isocitrate dehydrogenase, ATRX, and 1p19q, and other molecular markers. Aim: Our aim is to review the histopathology of diagnosed brain tumors and correlate with immunohistochemical (IHC) findings to note for any disparity to reform the diagnosis in order to benefit the patient and report to the clinician if any treatment change is to be considered. Materials and Methods: This article is based on studies of screening and diagnostic test. A total of 150 brain tumors were retrospectively analyzed. Age, gender, and the tumor histological type and grade were systematically recorded. We compared our histopathological diagnosis before the introduction of the WHO 2016 molecular classification of central nervous system tumors and later after the relevant IHC and fluorescence in situ hybridization studies. Statistical Analysis: The statistical analysis was done by using Statistical Package for Social Sciences version recent for Windows. Results: Out of the total 150 brain tumor patients, 65 were males and 45 were females. About 37 were glial and the rest were in other categories. Conclusions: The molecular diagnosis that substantiated with the histomorphology is more objective and beneficial in the treatment of the patients.

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Streptococcus pneumoniae Meningoencephalitis With Bilateral Basal Ganglia Necrosis

Journal of Child Neurology ◽

10.1177/0883073811409223 ◽

2011 ◽

Vol 26 (11) ◽

pp. 1438-1443 ◽

Cited By ~ 4

Author(s):

Jessy Magnus ◽

Paul M. Parizel ◽

Berten Ceulemans ◽

Patrick Cras ◽

Marloes Luijks ◽

...

Keyword(s):

Streptococcus Pneumoniae ◽

Basal Ganglia ◽

Neurological Impairment ◽

Hemorrhagic Transformation ◽

Exceptional Case ◽

Mr Images ◽

Caudate Nuclei ◽

Magnetic Resonance Imaging Mri ◽

The Brain ◽

Severe Neurological Impairment

Streptococcus pneumoniae ( S pneumoniae) is a common cause of bacterial meningitis, frequently leading to death or severe neurological impairment. We report an exceptional case of a 4-month-old boy with meningitis caused by S pneumoniae. Computed tomography (CT) and magnetic resonance imaging (MRI) examinations of the brain showed bilateral symmetrical necrosis involving the lentiform and caudate nuclei, as well as the thalamus. T1-weighted MR images showed patchy areas of increased signal intensity, consistent with hemorrhagic transformation of the lesions. Autopsy revealed widespread necrosis of the basal ganglia with clear signs of vasculitis. Severe complications of S pneumoniae meningoencephalitis are known in infants but to our knowledge, such lesions in the basal ganglia have only been reported thrice in adults and never in children.

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Classification of Tumors in Brain MRI Images With Hybrid of Global and Local DWT Features using Decision Tree

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c4659.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 3072-3077

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Decision Tree ◽

Brain Mri ◽

Extraction Process ◽

Discrete Wavelet ◽

Global Features ◽

Tumor Identification ◽

The Brain

Automated brain tumor identification and classification is still an open problem for research in the medical image processing domain. Brain tumor is a bunch of unwanted cells that develop in the brain. This growth of a tumor takes up space within skull and affects the normal functioning of brain. Automated segmentation and detection of brain tumors are important in MRI scan analysis as it provides information about neural architecture of brain and also about abnormal tissues that are extremely necessary to identify appropriate surgical plan. Automating this process is a challenging task as tumor tissues show high diversity in appearance with different patients and also in many cases they tend to appear very similar to the normal tissues. Effective extraction of features that represent the tumor in brain image is the key for better classification. In this paper, we propose a hybrid feature extraction process. In this process, we combine the local and global features of the brain MRI using first by Discrete Wavelet Transformation and then using texture based statistical features by computing Gray Level Co-occurrence Matrix. The extracted combined features are used to construct decision tree for classification of brain tumors in to benign or malignant class.

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