Identification And Classification Of Brain Tumor Images Using Efficient Classifier

The main objective of this study is to propose a model for finding brain tumor. Failing to detect the tumor in its prior stage will increases the chance of losing a life. So the identification and treatment for the tumor in its prior stages become vital to save the life of a human being. This work uses the Magnetic Resonance Image (MRI) images to identify and classify the benign and malign type brain tumors. Low pass filter is applied to preprocess the MRI image that removes the unwanted background structures at the same time keeps the important portions sharpened. Watershed segmentation method is used for segmenting the tumor affected area independently. The statistical feature extraction method Gray Level Co-occurrence Matrix (GLCM) is applied to take out the imperative features from the segmented tumor. The feature selection is performed using Recursive Feature Elimination- Particle Swarm Optimization (RFE-PSO) method. Ensemble Support Vector Machine (SVM) is applied to classify the tumors into harmless and harmful from the medical image.

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Klasifikasi Kanker Paru Paru menggunakan Naïve Bayes dengan Variasi Filter dan Ekstraksi Ciri GLCM

INDONESIAN JOURNAL OF APPLIED PHYSICS ◽

10.13057/ijap.v11i2.53213 ◽

2021 ◽

Vol 11 (2) ◽

pp. 256

Author(s):

Mohtar Yunianto ◽

Soeparmi Soeparmi ◽

Cari Cari ◽

Fuad Anwar ◽

Delta Nur Septianingsih ◽

...

Keyword(s):

Naive Bayes ◽

Median Filter ◽

Naïve Bayes ◽

Gray Level ◽

Pass Filter ◽

Low Pass Filter ◽

Otsu Thresholding ◽

Low Pass ◽

Occurrence Matrix ◽

High Pass

<p class="AbstractText">Telah berhasil dilakukan klasifikasi kanker paru-paru dari 120 data citra CT Scan. Pada penelitian, proses preposisi dimulai dengan variasi filtering yaitu low pass filter, median filter, dan high pass filter. Segmentasi yang digunakan yaitu Otsu Thresholding yang kemudian teksturnya akan diekstraksi menggunakan fitur Gray Level Co-occurrence Matrix (GLCM) dengan variasi arah sudut. Hasil dari ekstraksi GLCM dijadikan database yang akan menjadi dataset untuk pengklasifikasian citra menggunakan klasifikasi naïve bayes. Hasil dari penelitian dengan 12 buah variasi diperoleh hasil variasi terbaik adalah median filter dengan arah sudut GLCM 0° menunjukkan tingkat akurasi yang paling tinggi sebesar 88,33 %.</p>

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A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM

Metrology and Measurement Systems ◽

10.1515/mms-2015-0025 ◽

2015 ◽

Vol 22 (2) ◽

pp. 251-262 ◽

Cited By ~ 9

Author(s):

Chaolong Zhang ◽

Yigang He ◽

Lei Zuo ◽

Jinping Wang ◽

Wei He

Keyword(s):

Analog Circuit ◽

Support Vector ◽

Band Pass Filter ◽

Pass Filter ◽

Low Pass Filter ◽

Op Amp ◽

Novel Approach ◽

Low Pass ◽

Diagnosis Accuracy ◽

Incipient Fault

Abstract Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.

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Effects of a Low-Pass Filter on the Classification of Protein Electron-Density Maps in Terms of Structural Polymorphism using Manifold Learning: A Simulation Study for Cryo-Electron Microscopy Experiments

Journal of the Physical Society of Japan ◽

10.7566/jpsj.88.094801 ◽

2019 ◽

Vol 88 (9) ◽

pp. 094801

Author(s):

Naoto Takano ◽

Takashi Yoshidome

Keyword(s):

Electron Microscopy ◽

Manifold Learning ◽

Simulation Study ◽

Structural Polymorphism ◽

Pass Filter ◽

Low Pass Filter ◽

Cryo Electron Microscopy ◽

Density Maps ◽

Low Pass

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Automatic parametric fault detection in complex analog systems based on a method of minimum node selection

International Journal of Applied Mathematics and Computer Science ◽

10.1515/amcs-2016-0045 ◽

2016 ◽

Vol 26 (3) ◽

pp. 655-668 ◽

Cited By ~ 3

Author(s):

Adrian Bilski ◽

Jacek Wojciechowski

Keyword(s):

Support Vector ◽

Svm Classifier ◽

Minimal Set ◽

Pass Filter ◽

Low Pass Filter ◽

Low Pass ◽

Parametric Fault ◽

Video Amplifier ◽

Analog Systems ◽

Complex Analog

Abstract The aim of this paper is to introduce a strategy to find a minimal set of test nodes for diagnostics of complex analog systems with single parametric faults using the support vector machine (SVM) classifier as a fault locator. The results of diagnostics of a video amplifier and a low-pass filter using tabu search along with genetic algorithms (GAs) as node selectors in conjunction with the SVM fault classifier are presented. General principles of the diagnostic procedure are first introduced, and then the proposed approach is discussed in detail. Diagnostic results confirm the usefulness of the method and its computational requirements. Conclusions on its wider applicability are provided as well.

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Overhead Line Fault Location Using Wavelet Packet Decomposition and Support Vector Regression

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.516-517.1396 ◽

2012 ◽

Vol 516-517 ◽

pp. 1396-1399

Author(s):

Chun Guo Fei ◽

Bai Li Su

Keyword(s):

Transmission Line ◽

Support Vector Regression ◽

Fault Location ◽

Wavelet Packet ◽

Support Vector ◽

Wavelet Packet Decomposition ◽

Overhead Line ◽

Pass Filter ◽

Low Pass Filter ◽

Low Pass

This paper realizes the fault location in overhead line by using wavelet packet decomposition (WPD) and support vector regression (SVR). All various types of faults at different locations and various fault inception angles on a 735kV-360km overhead line power system are used. The system only utilizes voltage signals with single-end measurements. WPD is used to extract distinctive features from 1/2 cycle of post fault signals after noises have been eliminated by low pass filter. A SVR is trained with features obtained from WPD and consequently used in precise location of fault on the transmission line. The simulation results show, fault location on transmission line can be determined rapidly and correctly irrespective of fault impedance.

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Automatic classification of MR brain tumor images using KNN, ANN, SVM and CNN

International Research Journal of Engineering IT & Scientific Research ◽

10.21744/irjeis.v3n1.895 ◽

2017 ◽

Vol 3 (1) ◽

pp. 36-44

Author(s):

Hema Rajini N

Keyword(s):

Brain Tumor ◽

Magnetic Resonance ◽

Nearest Neighbor ◽

Magnetic Resonance Images ◽

Support Vector ◽

K Nearest Neighbor ◽

Network Support ◽

First Order ◽

Occurrence Matrix

A brain tumor classification system has been designed and developed. This work presents a new approach to the automated classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumors based on first order statistics and gray level co-occurrence matrix, in magnetic resonance images. The magnetic resonance feature image used for the tumor detection consists of T2-weighted magnetic resonance images for each axial slice through the head. To remove the unwanted noises in the magnetic resonance image, median filtering is used. First order statistics and gray level co-occurrence matrix-based features are extracted. Finally, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks are used to classify the brain tumor images. The application of the proposed method for tracking tumor is demonstrated to help pathologists distinguish its type of tumor. A classification with an accuracy of 89%, 90%, 91% and 95% has been obtained by, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks.

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