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Breast Cancer Detection Using Random Forest Classifier
- Publication Year :
- 2022
- Publisher :
- IGI Global, 2022.
-
Abstract
- 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.
- Subjects :
- Computer science
business.industry
Pattern recognition
02 engineering and technology
medicine.disease
Random forest
03 medical and health sciences
0302 clinical medicine
Breast cancer
030220 oncology & carcinogenesis
0202 electrical engineering, electronic engineering, information engineering
medicine
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....34fded46073cd575de256a5d0400aac3