scholarly journals A Complex Diffusion Based Modified Fuzzy C- Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection

2020 ◽  
Vol 34 (4) ◽  
pp. 419-427
Author(s):  
Subodh Srivastava ◽  
Guddu Kumar ◽  
Ritesh K. Mishra ◽  
Niharika Kulshrestha
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Breast Cancer Detection ◽  
Fuzzy C Means ◽  
Complex Diffusion

Breast Cancer Detection Using Random Forest Classifier

2021 ◽  
pp. 85-98
Author(s):  
Pavithra Suchindran ◽  
Vanithamani R. ◽  
Judith Justin
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Cancer Detection ◽  
Region Of Interest ◽  
Texture Features ◽  
Speckle Noise ◽  
Random Forest Classifier ◽  
Breast Cancer Detection ◽  
K Nearest Neighbors ◽  
Cad System

Breast cancer is the second most 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) filter. The goal of segmentation is to locate the region of interest (ROI) and active contour-based segmentation and fuzzy C means segmentation (FCM) are 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.

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