A Review on Textural Features Based Computer Aided Diagnostic System for Mammogram Mass Classification Using GLCM &amp; RBFNN

Tripathi, Nishant; Panda, Supriya P.

doi:10.14445/22315381/ijett-v17p290

Cited by 6 publications

(4 citation statements)

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“…The Detection of lung cancer various contributions have been made in past few year. Shiying Hu et al [8] proposed a work using dip and Gabor filter within Gaussian rule for detection of lung cancer. Three types of Enhancement techniques is used that is Auto Enhancement, Gabor filter, fast Fourier transform.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer System for Perception of Lung Cancer

2019

IJITEE

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Detection of cancer in its previous stages to increases survival rate of patient. CAD system is efficient because it take minimum time to detect weather the patient has cancer or not. It is very difficult for detection of lung cancer its earlier stage as it takes many tests. There are many of the CAD system which is designed for earlier detection of tumors. Many CAD systems have been designed in past for early detection of lung tumor. For segmentation purpose Thresholding is used and detection of area in which suspected tumor part growing algorithms is used. There are various factor is calculated using GLCM. Multilayer feed forward BPNN approaches for classify the feature set. Performance is calculated in form of mean square error (MSE) using BPNN. The CAD (computer aided diagnosis) model gives 90% true count. For implementation purpose MATLAB is used.

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Section: Literature Reviewmentioning

confidence: 99%

Computer System for Perception of Lung Cancer

2019

IJITEE

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“…The pertinence of CAD algorithms and the process of screening for the detection of abnormalities in an early stage is very important [2]. Computer-aided techniques help the Radiologists in mammography analysis for early breast cancer detection.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Method of Local Binary Pattern and Classiﬁcation Tree for Early Breast Cancer Detection by Mammogram Classiﬁcation

2019

IJRTE

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- Breast cancer is an alarming disease due to a mutation in breast cells and it is one type of cancer among women which highly leads to their death. One of the most effective tools for early detection of breast cancer is mammography, which is a screening tool used to examine the human breast by using low-dose amplitude X-rays. Computer-Aided Diagnosis (CAD) is used as an important tool to help the medical professionals for classifying breast tissues into a different class. Computer-Aided Diagnosis (CAD) can be used to reduce human error in reading the mammograms and it shows effective results in the classification of benign and malignant abnormalities. The proposed method presents a new classification approach to detect the abnormalities in mammograms using Local Binary Pattern and Decision Tree Classification. A Uniform Local Binary Pattern(uLBP) is an extension of the original Local Binary Pattern in which only patterns that contain at most two transitions from 0 to 1 (or vice versa) are considered. In uniform Local Binary Pattern (LBP) mapping, there is a separate output label for each uniform pattern and all then on uniform patterns are assigned to a single label. These patterns are utilized to detect breast cancer by classification employing the Decision Tree Classification. Specificity and sensitivity are the two statistical measures used in this proposed method to verify and measure the significance of the test related to abnormalities in the breast tissues. Thus, it can be a measurement of performance tests for classifying the patients who do and do not suffer from cancer. The mini-MIAS mammography database is employed for testing the accuracy of the proposed method and the results are promising.

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“…It mostly found in woman [1]. This dangerous illness is caused by lesion that can be classified into two categories, which are benign and malignant.…”

Section: Introductionmentioning

confidence: 99%

“…Most of breast cancer patient do not notice about its presence and died before they get proper medication. Thus, breast cancer detection on the early stage is necessary in order to reduce the number of death [1].…”

Section: Introductionmentioning

confidence: 99%

Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network

Pratiwi

Alexander

Harefa

et al. 2015

Procedia Computer Science

122

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Computer Aided Diagnosis (CAD) is used to assist radiologist in classifying various type of breast cancers. It already proved its success not only in reducing human error in reading the mammograms but also shows better and reliable classification into benign and malignant abnormalities. This paper will report and attempt on using Radial Basis Function Neural Network (RBFNN) for mammograms classification based on Gray-level Co-occurrence Matrix (GLCM) texture based features. In this study, normal and abnormal breast image used as the standard input are taken from Mammographic Image Analysis Society (MIAS) digital mammogram database. The computational experiments show that RBFNN is better than Back-propagation Neural Network (BPNN) in performing breast cancer classification. For normal and abnormal classification, the result shows that RBFNN's accuracy is 93.98%, which is 14% higher than BPNN, while the accuracy of benign and malignant classification is 94.29% which is 2% higher than BPNN.

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A Review on Textural Features Based Computer Aided Diagnostic System for Mammogram Mass Classification Using GLCM & RBFNN

Cited by 6 publications

References 0 publications

Computer System for Perception of Lung Cancer

Computer System for Perception of Lung Cancer

Hybrid Method of Local Binary Pattern and Classiﬁcation Tree for Early Breast Cancer Detection by Mammogram Classiﬁcation

Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network

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