A novel approach for detection of breast cancer at an early stage using digital image processing techniques

Sangeetha, R.; Murthy, K. Srikanta

doi:10.1109/icisc.2017.8068625

Cited by 18 publications

(9 citation statements)

References 5 publications

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“…A novel hybrid approach of digital image processing was adopted to analyze the mammograms. Using this novel approach, the early identification of breast cancer at the stage of micro calcification was achieved leading to the higher accuracy of proposed technique [5]. Digital image based elasto-tomography was developed as a prototype for the evaluation of breast cancer.…”

Section: A Existing Methods For the Identification Of Malignantmentioning

confidence: 99%

Proficiency Assessment of Machine Learning Classifiers: An Implementation for the Prognosis of Breast Tumor and Heart Disease Classification

Talha¹,

Kushsairy²,

Shahzad³

et al. 2020

IJACSA

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Breast cancer and heart disease can be acknowledged as very dangerous and common disease in many countries including Pakistan. In this paper classifiers comparative study has been performed for the tumor and heart disease classification. Around one lac women are diagnosed annually with this life-threatening disease having no family history of the disease. If it is not treated on time it may grow and spread to the other parts of human body. Mammograms are the X-rays of the breast which can be used for the screening of cancer tumor. Prior identification of breast cancer may increase the chance of survival up to 70 percent. Tumors which causes cancer can be categorized into two types: a) Benign and b) Malignant. Benign tumor can be explained as the tumor which are not attached to neighbor tissues or spread in the other parts of the body. In Malignant tumor, other parts may be affected by it as it can grow and spread in the other parts of the body. To classify the tumor as Malignant or Benign is very complex as the similarities of cancer tumor and tumor caused by the skin inflammation are almost same. The early identification of Malignant is mandatory to protect the patient life. Diversified medical methods based on deep learning and machine learning have been developed to treat the patients as cancer is a very serious and crucial issue in this era. In this research paper machine learning algorithms like logistic regression, K-NN and tree have been applied to the breast cancer data set which has been taken from UCI Machine learning repository. Comparative study of classifiers has been performed to determine the better classifier for the robust prediction of breast tumors. Simulated results proved that using Logistic regression, ninety-one percent accuracy was achieved. The research showed that logistic regression can be applied for the accurate and precise early prediction of breast cancer. Cardiovascular disease is very common throughout the world. It has been noticed that health in cardiac patients that there are so many factors which causes heart disease or heart attack. The factors leading to the heart failure includes varying blood pressure, high sugar, cardiac pain, and heart rate, high cholesterol level (LDL), artery blockage and irregular ECG signals. Many researchers proved that stress in patients can also be the reason for the heart disease. Higher numbers of cardiac surgeries like angioplasty and heart bypass are performed on annual basis. Actually, people don't care about their lifestyle and diet and fully ignore the symbols. It can be early predicted and cured if proper testing and medication for heart is done. Sometimes there is a false pain which has the same feeling like angina pain depicting cardiovascular disease. To reduce the false alarm and robustly classify the heart disease, several machine learning approaches have been adopted. In proposed research for the accurate classification of heart disease comparison has been performed among support vector machine (SVM), K-nearest neighbors K-NN and lin...

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Section: A Existing Methods For the Identification Of Malignantmentioning

confidence: 99%

Proficiency Assessment of Machine Learning Classifiers: An Implementation for the Prognosis of Breast Tumor and Heart Disease Classification

Talha¹,

Kushsairy²,

Shahzad³

et al. 2020

IJACSA

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“…Sangeetha R et al, [2] The feature extraction process in this system is done by using GLCM (Gray Level Co-Occurrence Matrix) and by the initial order statistics features. To divide the mammographs as abnormal or normal, two divided operations are classified by using KNN algorithm (k-nearest neighbor) to detect the early stage of the tumor and also to find the accuracy of tumor region.…”

Section: Related Workmentioning

confidence: 99%

“…With the additional selection of M-health, E-health, and acceptable innovations, the volume of information is expanding at an enormous value. This incorporates computerized wellbeing to store data, imaging and processing data, victim engender data, sensor data, and other configurations of data [2].…”

Section: Introductionmentioning

confidence: 99%

“…MATLAB is a name that stands for the matrix laboratory to provide an easy way of use and access to perform matrix using the development by the LIN pack (Linear System Package) and EIS pack (EIGN system package) project. Today, MATLAB engines incorporate the LA pack (Linear Algebra Package) and BLAS (Basic Linear Algebra Subprograms) libraries, creating software for computing matrices [2].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Breast Cancer using Digital Image Processing Techniques

2019

ijrte

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Breast cancer, a major public health contention and currently causes an increased rate of cancer death in women. The preeminent intent of this project in medical diagnostics is by using mammography, that is a unique imaging technique in medicine for examining the breasts. A higher quality mammographic images (for example electronic pictures) are stored, mammography method (i.e exam) is performed, which is a prior stage for detection and diagnosis of bosom’s malignant growth. In order to detect the tumor FCM algorithm for segmentation is used and the features are extracted by using multi-level wavelet transformation technique with PCA and then some features are added with GLCM features. Further, those segmented region features are extracted and the dataset is trained and tested completely. The images are classified by using SVM, KNN, Tree classifier, Neural Networks or Naive Bayes classifier. Finally, the images from Kaggle dataset are compared and categorized as normal, benign or malignant tumors.

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“…In total cases of cancer in women, 23% of them suffer from the breast cancer and approximately 1.6 million new cases are observed almost every year. Moreover the early detection of cancer can reduce the death rate and increase the more chances of survival, Breast Cancer is detected as well diagnosed by integrating the several approaches such as physical examination and imaging, the technique used for detecting are known as mammography and the images are firmly known as mammograms [2].…”

Section: Introductionmentioning

confidence: 99%

Breast Segmentation and Probable Region Identification for Breast Cancer using DL-CNN

Kumar M*,

Jatti,

Narayanappa

2019

IJITEE

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Mammography is one of the key method used for detecting the breast cancer, several researcher has proposed the detection and segmentation method, however absolute solution has not developed till now and they have certain limitation and still it is one of the major challenge for finding the region in masses. Hence in this research work we have developed and design a novel method named as DL-CNN (Dual Layered) architecture CNN. The main intention of the model is segmentation and probable region identification. DL-CNN is based on the Convolution Neural Network. It has two layer first layer is applied for identifying the probable region whereas the second layer is used for segmentation and minimizing the false positive Reduction. In order to evaluate the DL-CNN algorithm by using the In Breast Dataset. Moreover the proposed model is compared against the various model in terms of ROI(Region of Interest), Dice_Index and False positive per Image. Result analysis shows that our model outperforms the existing

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A novel approach for detection of breast cancer at an early stage using digital image processing techniques

Cited by 18 publications

References 5 publications

Proficiency Assessment of Machine Learning Classifiers: An Implementation for the Prognosis of Breast Tumor and Heart Disease Classification

Proficiency Assessment of Machine Learning Classifiers: An Implementation for the Prognosis of Breast Tumor and Heart Disease Classification

Detection of Breast Cancer using Digital Image Processing Techniques

Breast Segmentation and Probable Region Identification for Breast Cancer using DL-CNN

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