breast-cancer-detection-using-image-processing-and-machine-learning
Mrs. K.Maheswari, Associate professor, Dept of ECE, Sanskrithi School of Engineering
T.S.Afreen, P.Seema Kousar, B. Vijay Lakshmi, K.Pavitra
Dept of ECE Sanskrithi school of engineering Puttaparthi 515134
Abstract: Nowadays breast cancer is the frequent type of cancer in women which leads to death. The mammography and ultrasound are the common ways to the breast cancer . Our paper describes Machine Learning for identification of breast cancer using mammography imagesUltrasound and Elastography are the combined imaging techniques to separate benign and malignant breast lesions.support vector machine is a classifier which is used to classification of combined B-mode andElastography image.. Our project helps the physician to detect the breast cancer earlier.
Keywords: Breast Cancer, Elastography, Image Processing, B-mode (ultra sound), SVM
Introduction:
Breast cancer constitutes a significant threat on women health and is considered the second leading cause of their death. Breast cancer is a result of an abnormal behavior in the functionality of the normal breast cells. Therefore, breast cells tend to grow uncontrollably forming a tumor which can be felt as a lump in the breast.The earlier detection of breast cancer leads to decreasing the number of deaths by applying relevant prescription.. In general, palpation, ultrasound and mammography are the most common ways of diagnosis. However,Nowadays Elastography and ultrasound techniques are playing a major role for the detection of breast cancer.
The Computer-aided diagnosis is a tool using a merge of ultrasound(B-mode) and elastography images gives a high quality of output rather than other digitalimaging techniques due to its exact value of classification . Machine learninggains a data by using mathematical and statistical models.
Machine learning finds an important role in biomedical applications in which accuracy of measurements is a crucial factor.Consequently machine learning algorithms can helps todetect the breast cancer at its initial stage. Machine learning tools can establish mostpredicative features from the noisy and complex datasets.
Whereas, for exceptional accuracy the false negative and false positive should be reduced.
Literature Survey:
B.M.Gayathri , C.P.Sumathi and T.Santhanam [1] cancer detection of breast Machine Learning Algorithm –ASurvey Machine Learning Algorithms are developed to reduce the time taken for the process of diagnosis and to decrease the death .This paper encapsulates the survey on breast cancer diagnosis with various machinelearning algorithms and techniques, which causes the improvement of accuracy and predictingthe cancer at earlier stage. We know The number of papers that areimplemented to diagnose the breast cancer by the help of these survey paper
S. Punitha, S. Ravi and M. Anousouya Devi [2] “Breast Cancer Detection in Digital Mammograms using Segmentation Techniques” Segmentation and Edge Detection algorithms Mammography is the adequate and method for the early diagnosis of the breast cancers through screening and accurate detection of masses, microcalcifications and architectural distortions. The breast cancer detection accuracy and efficiency can be increased by applying various image analysis techniques on digital mammograms on the dense regions of the breasts helping the radiologists to identify suspicious regions preventing unwanted biopsies and traumatic treatments. This paper focuses on the various image analysis techniques such as segmentation and edge detection algorithms for the detection breast abnormalities and compares its advantages and disadvantages.
Ashmithakhaleel khan &Naufal p [3] Automatic lesion detection based on Wavelet usingMean Filter, Adaptive Mean filter, Weiner Filter. In thispaper, the authors recommended a new method for the segmentation process that helps to detect the lesions.In this method Pre-processing and DWT of image can be done before segmentation.
Preprocessing plays a vital role in medical images to remove the unwanted noise and for increasing the intensity of image. Discrete Wavelet Transformation that is used for segmentation because it contains most accurate information of the input image.
Amandeep Singh, Amanpreetkaur [4] Breast tumor detection using segmentation technique from CT scan Crop Segmentation, Noise Reduction, Edge Detection, Global Thresholding .They presented the imaging technique is used to detect the information of the tissue, the bio medical images having the capability to help the physician in detecting the disease which is caused by abnormal growth of the cells. The evolving software and algorithms is used to analyze the images also helps the physician in their works. The clue and rigid is auto extracting of the small modules or tumor from the bio medical imagethat is identified at starting stage that gives the information of the early cancer. This work merges the image threshold, edge detection and segmentation helps in detection of cancer.
breast-cancer-detection-using-image-processing-and-machine-learning
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