Automated Detection for Human Cancer Cell is one of the most effective applications of image processing and has obtained great attention in latest years, therefore. In this study, we 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 cells in the breast biopsy. The images are exposed to a series of pre-processing steps, which include resizing image such as 1024*1024, 512*512, enhance images by remove noise through (Median Filter ? Wiener 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 clustering) and region-based segmentation (Watershed). Shape, Texture and Color features are extracted for Detection. The results show high Detection Rate for breast cancer cells images either (Benign or Malignant).