Comparing Classification Techniques to Detect Breast Tumour (original) (raw)
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Comparing Classification Techniques to Detect Breast Tumour Passant
2014
Breast tumour detection in ultrasound images has been a challenge due to the presence of different kinds of noise caused by various factors. The focus of this research is the design, implementation andperformance evaluation ofseveral tumour detection systems based on different classifiers and using ultrasound breast images. First, Gaussian and anisotropic diffusion filters are applied to remove additive and speckle noise, respectively, and histogram equalization is used for image enhancement. Second, textural features are extracted from the input image followed by principal component analysis to reduce the dimensionality of the data set. Finally, the classification process is performed using two different classifiers includingsupport vector machine (SVM) andBootstrap aggregating (bagging) on REP tree. A comparison of the performance of these classifiers is presented.
Computer aided breast cancer detection using ultrasound images
Materials Today: Proceedings, 2020
Breast cancer is the second prevalent type of cancer among women. Breast Ultrasound (BUS) imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities in the breast. To improve the diagnostic accuracy, Computer Aided Diagnosis (CAD) system is helpful for breast cancer detection and classification. Normally, a CAD system consists of four stages: pre-processing, segmentation, feature extraction, and classification. In this chapter, the pre-processing step includes speckle noise removal using Speckle Reducing Anisotropic Diffusion (SRAD). The goal of segmentation is to locate the Region of Interest (ROI) and Active contour-based segmentation is used in this work. The texture features are extracted and fed to a classifier to categorize the images as Normal, Benign and Malignant. In this work three classifiers namely K-Nearest Neighbors (KNN) algorithm, Decision tree algorithm and Random Forest classifier are used and the performance is compared based on the accuracy of classification.
2016
Breast cancer is the second leading cause of death in women after heart diseases. A well-known statement in cancer society is “Early detection means better chances of survival”. In the past few years several techniques were developed to detect breast tumors in early stages. A proposed system is designed for breast tumors detection using ultrasound images. Ultrasound is used because it is less expensive and less invasive than X-rays used in mammography and computerized tomography. It can provide a second opinion for a physician to detect breast tumors. The proposed system consists of three main steps: preprocessing, feature extraction and classification. Gaussian blurring, anisotropic diffusion and histogram equalization are used to reduce additive noise, speckle noise and to enhance the image quality respectively. The second step is feature extraction and dimensionality reduction. PCA is used to reduce the dimensions of the feature vector. The third and final step is the classificat...
Detection of Breast Cancer Using Ultrasound Images
Background: Breast cancer is one of the leading causes of cancer death in women around the world. In order to reduce the death rate, the tumors have to be detected at the early stage. The proposed system is a new approach with automatic contouring and texture analysis to aid in the classification of Breast Lesion using Ultrasound. Firstly, the goal of removing the speckle while preserving important information from the lesion boundaries, anisotropic diffusion filtering is applied to the ultrasonic image. A marker-controlled watershed transform is used for image segmentation, automatically extracts the precise contour of breast lesions. 24 Gray Level Co-occurrence Matrix (GLCM) features are extracted from the extracted lesion. Support Vector Machine (SVM) classifier utilizes the selected feature vectors to identify the breast lesion as benign or malignant. A confusion matrix is used to describe the performance of a classification model on a set of test data for which the true values are known. It allows the visualization of the performance of an algorithm.
Classification of Mass in Breast Ultrasound Images using Image Processing Techniques
International Journal of Computer …, 2012
This work presents a new approach for classifying masses in breast ultrasound images. Detection and classification of masses in ultrasound images still remains a challenge because most of the ultrasound images contain speckle noise and fuzzy boundaries. Ultrasound (US) is an important adjunct to mammography in breast cancer detection as it increases the rate of detection in dense breasts. Ultrasound also does dynamic analysis of moving structures in breast thus it is used to analyze the functional behavior of breast. In the proposed method, ultrasound images are preprocessed using Gaussian smoothing to remove additive noise and anisotropic diffusion filters to remove multiplicative noise (speckle noise). Active contour method has been used to extract a closed contour of filtered image which is the boundary of the spiculated mass. Spiculations which make breast mass unstructured or irregular are marked by measuring the angle of curvature of each pixel at the boundary of mass. To classify the breast mass as malignant or benign we have used: the structure of mass in accordance with spiculations, elliptical shape of the mass and acoustic shadowing feature which is an important functional feature. We have used receiver operating characteristic curve (ROC) to evaluate the performance. We have validated the proposed algorithm on 100 sub images(40 spiculated and 60 non spiculated) and results shows 92.7% of sensitivity with 0.88 Area Under Curve. Proposed techniques were compared and contrasted with the existing methods and result demonstrates that proposed algorithm has successfully detected and classified mass ROI candidates in breast ultrasound images.
2022 7th International Conference on Image and Signal Processing and their Applications (ISPA)
The present paper deals with breast tumors classification from ultrasound images. The proposed procedure consists of four steps, namely preprocessing, segmentation, feature extraction and classification. To improve the quality of ultrasound images, the preprocessing step consists of anisotropic filtering and histogram equalization that are performed on the original images. The segmentation is performed on the preprocessed images using the Level Set method that allows to extract the region of interest (ROI) and to reduce its size at the same time. Two feature extraction methods are used in this work namely, the local binary pattern (LBP) method and the histogram of oriented gradients (HOG) method. The two methods (LBP and HOG) are techniques of textures analysis and allow to characterize the ROI. The extracted feature sets constitute the inputs for three classifiers namely, support vector machines (SVM), k-nearest neighbors (KNN) and decision trees (DT). In this work, the best results are obtained by the concatenation of the two feature vectors namely LBP and HOG associated to the SVM classifier. This allows to achieve an accuracy of 96%, a sensitivity of 97% and a specificity of 94%.
An automatic system for classification of breast cancer lesions in ultrasound images
2014
Breast cancer is the most common of all cancers and second most deadly cancer in women in the developed countries. Mammography and ultrasound imaging are the standard techniques used in cancer screening. Mammography is widely used as the primary tool for cancer screening, however it is invasive technique due to radiation used. Ultrasound seems to be good at picking up many cancers missed by mammography. In addition, ultrasound is non-invasive as no radiation is used, portable and versatile. However, ultrasound images have usually poor quality because of multiplicative speckle noise that results in artifacts. Because of noise segmentation of suspected areas in ultrasound images is a challenging task that remains an open problem despite many years of research. In this research, a new method for automatic detection of suspected breast cancer lesions using ultrasound is proposed. In this fully automated method, new de-noising and segmentation techniques are introduced and high accuracy ...
Automated breast cancer detection and classification using ultrasound images: A survey
Pattern Recognition, 2010
Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.
Breast tumor classification in ultrasound images using support vector machines and neural networks
Research on Biomedical Engineering, 2016
Introduction: The use of tools for computer-aided diagnosis (CAD) has been proposed for detection and classification of breast cancer. Concerning breast cancer image diagnosing with ultrasound, some results found in literature show that morphological features perform better than texture features for lesions differentiation, and indicate that a reduced set of features performs better than a larger one. Methods: This study evaluated the performance of support vector machines (SVM) with different kernels combinations, and neural networks with different stop criteria, for classifying breast cancer nodules. Twenty-two morphological features from the contour of 100 BUS images were used as input for classifiers and then a scalar feature selection technique with correlation was used to reduce the features dataset. Results: The best results obtained for accuracy and area under ROC curve were 96.98% and 0.980, respectively, both with neural networks using the whole set of features. Conclusion: The performance obtained with neural networks with the selected stop criterion was better than the ones obtained with SVM. Whilst using neural networks the results were better with all 22 features, SVM classifiers performed better with a reduced set of 6 features.
2014
The number of Breast cancer has been increasing over the past three decades. Early detection of breast cancer is crucial for an effective treatment. Mammography is used for early detection and screening. Especially for young women, mammography procedures may not be very comfortable. Moreover, it involves ionizing radiation. Ultrasound is broadly popular medical imaging modality because of its non-invasive, real time, convenient and low cost nature. However, the excellence of ultrasound image is corrupted by a speckle noise. The presence of speckle noise severely degrades the signal-to noise ratio (SNR) and contrast resolution of the image. Therefore speckle noise need to be reduced before extracting the features. In this research focus on developing an algorithm to reduce the speckle noise, feature extraction and classification methods for benign and malignant tumors showed that SVM-Polynomial classification produces a high classification rate (77%) for Grey level Co-occurrence matr...