الفهرس 
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Abstract
The goal of this research is to propose an automated detection system for human cancer cells based on breast cancer cells. This study was conducted on a set of Fine Needle Aspiration (FNA) biopsy microscopic images that have been obtained from the “Pathology Center - Faculty of Medicine - Mansoura University Hospital - Egypt” is made up of 72 microscope image samples of benign, 72 microscope image samples of malignant. The aim of this study is to distinguish benign from malignant tumors of breast cells. The images are exposed to series of pre-processing steps, which include resize image such as 1024*1024, 512*512, enhancement images by remove noise through (Median Filter) and contrast enhancement through (Unsharp Masking – Adjust Intensity). This process is evaluated by Peak signal-to-noise ratio (PSNR) and Mean Square Error (MSE). The system depends on breast cancer cells detection using clustering-based segmentation (K-means clustering, Fuzzy C-means clustring) and edge-based segmentation (Watershed). Shape, Texture and Color features are extracted for classification. Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Back-Propagation Artificial Neural Networks (BPNNs) are employed for classification. The results show that final performance evaluation based on 142-microscopic images finding out either (benign or malignant) which was successfully discovered by the system. Finally, show the best performance from all these techniques. The results show high accuracy rate.