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IJSTR >> Volume 5 - Issue 9, September 2016 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Microscopic Image Processing Of Automated Detection And Classification For Human Cancer Cell

[Full Text]



Laith Muayyad Abdul-Hameed Al-Hayali, Dr. M. Morsy, M. M. Abdul-Razak



Digital Image Processing, Breast Cancer Cells, Contrast Enhancement, K-Means Clustering, Fuzzy C-Means, Watershed, Features Extraction, Classification, SVM, K-NN, Back Propagation Neural Networks.



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 purpose of this study is to detect and classify the benign and 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) and contrast enhancement through (Unsharp Masking – Adjust Intensity). 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). Finally classification stage by using (Support Vector Machine, K-Nearest Neighbors and Back-Propagation Neural Networks). The final classification with the best accuracy in SVM is (97.22%), in K-NN and BPNNs is (98.61%).



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