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

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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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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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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Predicting Physico-Chemical Characteristics of Brain Tumour

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9213 ◽

2020 ◽

Vol 17 (8) ◽

pp. 3473-3477

Author(s):

M. S. Roobini ◽

T. V. L. Bharathi ◽

T. Aishwaya Sailaja ◽

M. Lakshmi ◽

Anitha Ponraj ◽

...

Keyword(s):

Brain Tumor ◽

Support Vector ◽

Tumor Study ◽

Physico Chemical ◽

Tumor Identification ◽

Three Stages ◽

Features Reduction ◽

The Brain ◽

Mri Image

This research proposes a series of algorithms that work for improved Brain Tumor identification and classification. The Brain Tumor study based on the MRI image will effectively resolve the classification method for diagnosis of brain tumors. There are three stages: Extraction of features, Reduction of features and classification. Extraction function and reduction of functionality used for two algorithms. The extracted characteristics are Mean, Standard deviation, Curtosis, Skewness, Entropy Contrast, Variance, Smoothness, Correlation and Power. The result is then supplied to Support Vector Machine (SVM) for the Benign or Malignant classification of tumours.

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Brain Tumor Extraction Using Matlab

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35989 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 4619-4625

Author(s):

P. Chandra Sandeep

Keyword(s):

Image Processing ◽

Brain Tumor ◽

Brain Mri ◽

Continuous Growth ◽

Infected People ◽

Abnormal Cells ◽

Tumor Extraction ◽

Crucial Part ◽

The Brain

The brain is the most crucial part of our human body which acts as central coordinating system for all the controlling and all regular functions of our body. The continuous growth of abnormal cells which creates certain mass of tissue is called as tumor. Tumor in the brain can be either formed inside the brain or gets into brain after formed at other part. But there is no clear information regarding the formation of brain tumor till date. Though the formation tumor in brain is not common or regular but the mortality rate of the infected people is very high because the brain is major part of body. So, it is very important get the treatment at the early stages of brain tumor but there is no direct procedure for detection and classification of tumor in the very first step of diagnosis. In actual medical diagnosis, mri images alone can’t be able to determine the detected tumor as either the cancerous or non-cancerous. But the tumor may be sometimes danger to life or may not be danger to life. Tumor inside the brain can be of either the benign(non- cancerous) or the malignant(cancerous). So, we need to detect the tumor from the MRI images through image processing and then to classify the detected tumor as it belongs to either the benign or malignant tumor. We are going to get the brain mri images as our dataset for our proposed method but the images we got may have the noise. So, we need to preprocess the image using the image preprocessing techniques. We are going to use several algorithms like thresholding, clustering to make the detection of tumor by using the image processing and image segmentation and after the detection of tumor we are going do feature extraction. This step involves the extraction of detected objects features using DWT. This extracted features are given as input to classifier algorithms like SVM’s and CNN after reduction of features using the PCA.

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Brain MRI Images Segmentation Based on U-Net Architecture

Iraqi Journal for Electrical And Electronic Engineering ◽

10.37917/ijeee.18.1.3 ◽

2021 ◽

Vol 18 (1) ◽

pp. 21-27

Author(s):

Assalah Atiyah ◽

Khawla Ali

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Performance Metrics ◽

Brain Mri ◽

Regular Function ◽

Survival Rates ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Edge Based ◽

The Brain

Brain tumors are collections of abnormal tissues within the brain. The regular function of the brain may be affected as it grows within the region of the skull. Brain tumors are critical for improving treatment options and patient survival rates to prevent and treat them. The diagnosis of cancer utilizing manual approaches for numerous magnetic resonance imaging (MRI) images is the most complex and time-consuming task. Brain tumor segmentation must be carried out automatically. A proposed strategy for brain tumor segmentation is developed in this paper. For this purpose, images are segmented based on region-based and edge-based. Brain tumor segmentation 2020 (BraTS2020) dataset is utilized in this study. A comparative analysis of the segmentation of images using the edge-based and region-based approach with U-Net with ResNet50 encoder, architecture is performed. The edge-based segmentation model performed better in all performance metrics compared to the region-based segmentation model and the edge-based model achieved the dice loss score of 0. 008768, IoU score of 0. 7542, f1 score of 0. 9870, the accuracy of 0. 9935, the precision of 0. 9852, recall of 0. 9888, and specificity of 0. 9951.

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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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Brain tumor diagnosis based on discrete wavelet transform, gray-level co-occurrence matrix, and optimal deep belief network

SIMULATION ◽

10.1177/0037549720948595 ◽

2020 ◽

Vol 96 (11) ◽

pp. 867-879

Author(s):

Li Xu ◽

Qi Gao ◽

Nasser Yousefi

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Tumor Cells ◽

Deep Belief Network ◽

Tumor Segmentation ◽

Discrete Wavelet ◽

Gray Level ◽

Belief Network ◽

Occurrence Matrix ◽

The Brain

Brain tumors are a group of cancers that originate from different cells of the central nervous system or cancers of other tissues in the brain. Excessive cell growth in the brain is called a tumor. Tumor cells need food and blood to survive. Growth and proliferation of tumor cells in the cranial space, cause strain inside the brain and thus disrupt vital human structures. Therefore, diagnosis in the early stages of brain tumors is crucial. This study introduces a new optimized method for early diagnosis of the brain tumor. The method has five main parts of noise reduction, tumor segmentation, morphology, feature extraction based on wavelet and gray-level co-occurrence matrix, and classification based on an optimized deep belief network. For optimizing the classifier network, an enhanced version of the moth search algorithm is utilized. Simulation results are applied to three different datasets, FLAIR, T1, and T2, and the accuracy results of the presented method are compared with two other metaheuristics, particle swarm optimization and Bat algorithms. The final results showed that the presented technique has good achievements toward the compared methods.

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Clustering of Brain Tumors in Brain MRI Images based on Extraction of Textural and Statistical Features

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v16i12.16929 ◽

2020 ◽

Vol 16 (12) ◽

pp. 116

Author(s):

Hamed Samadi Ghoushchi ◽

Yaghoub Pourasad

Keyword(s):

Magnetic Resonance Imaging ◽

Brain Tumor ◽

Magnetic Resonance ◽

Brain Tumors ◽

Imaging Techniques ◽

Brain Mri ◽

Support Vector ◽

Statistical Features ◽

Resonance Imaging ◽

The Brain

The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper, classification methods for MRI images of tumors of the human brain are performed to review the astrocytoma-containing glands. Methods used to classify brain tumors, including preprocessing, screening, tissue extraction, and statistical features of the tumor using two types of T1W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic C-Means Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis.

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A Comparative Study of Enhanced Machine Learning Algorithms for Brain Tumor Detection and Classification

10.36227/techrxiv.16863136 ◽

2021 ◽

Author(s):

ANKIT GHOSH ◽

ALOK KOLE

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Brain Tumors ◽

Binary Classification ◽

Brain Mri ◽

Random Forest Classifier ◽

The Other ◽

Gradient Boosting ◽

Multi Class Classification

The improvement of Artificial Intelligence (AI) and Machine Learning (ML) can help radiologists in tumor diagnostics without invasive measures. Magnetic resonance imaging (MRI) is a very useful method for diagnosis of tumors in human brain. In this paper, brain MRI images have been analyzed to detect the regions containing tumors and classify these regions into three different tumor categories: meningioma, glioma, and pituitary. This paper presents the implementation and comparison of various enhanced ML algorithms for the detection and classification of brain tumors. A brain tumor is the growth of abnormal cells in the human brain. Brain tumors can be cancerous or non-cancerous. Cancerous or malignant brain tumors can be life threatening. Hence, detection and classification of brain tumors at an early stage is extremely important. In this paper, enhanced ML algorithms have been implemented to predict the presence or the absence of brain tumors using binary classification and to predict whether a patient has brain tumor or not and if he does, detect the type of brain tumor using multi-class classification. The dataset that has been used to perform the binary classification task comprises of two types of brain MRI images with tumor and without tumor. Here nine ML algorithms namely, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT) classifier, Random Forest classifier, XGBoost classifier, Stochastic Gradient Descent (SGD) classifier and Gradient Boosting classifier have been used to classify the MRI images. A comparative analysis of the ML algorithms has been performed based on a few performance metrics such as accuracy, recall, and precision, F1-score, AUC-ROC curve and AUC-PR curve. Gradient Boosting classifier has outperformed all the other algorithms with an accuracy of 92.4%, recall of 94.4%, precision of 85%, F1-score of 89.5%, AUC-ROC of 97.2% and an AUC-PR of 91.4%. To address the multi-class classification problem, four ML algorithms namely, SVM, KNN, Random Forest classifier and XGBoost classifier have been employed. In this case, the dataset that has been used consists of four types of brain MRI images with glioma tumor, meningioma tumor, and pituitary tumor and with no tumor. The performances of the ML algorithms have been compared based on accuracy, recall, precision and the F1-score. XGBoost classifier has surpassed all the other algorithms in terms of accuracy, precision, recall and F1-score. XGBoost has produced an accuracy of 90%, precision of 90%, and recall of 90% and F1-score of 90%.

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