WRSTA, 13 August, 2006 Rough Sets in Hybrid Intelligent Systems For Breast Cancer Detection By Aboul Ella Hassanien Cairo University, Faculty of Computer and Information, IT Dept.

WRSTA, 13 August, 2006 Outline Introduction  Digital mammography  Hybrid intelligent systems Objective What is Mammogram? Mammogram Analysis Framework  Pre-processing phase  Segmentation  Feature Extraction phase  Feature Representation phase  Generated Rules phase  Classification phase Hybrid Intelligent System  Pre-processing Algorithm – Fuzzy Image Processing  Rough Set data analysis  Rough neural Classifier  Evaluation Results Conclusion and Future Work

WRSTA, 13 August, 2006 Introduction According to the National Cancer Institute:  Breast cancer is the leading cause of cancer deaths in women today and it is the most common type of cancer in women.  Each year about 180,000 women in the United States develop breast cancer, and  About 48,000 lose their lives to this disease.  It is also reported that a woman's lifetime risk of developing breast cancer is one in eight. Currently, digital mammography is one of the most promising cancer control strategies in earliest stages.

WRSTA, 13 August, 2006 What is a mammograms? A mammogram is a special kind of X-ray that allows the doctor to see into the breast tissue

WRSTA, 13 August, 2006 Introduction Hybridization of intelligent systems is  A promising research field of modern artificial intelligence concerned with the development of the next generation of intelligent systems.  A fundamental stimulus to the investigations of Hybrid Intelligent Systems (HIS) is the awareness in the academic communities that combined and integrated approaches will be necessary if the remaining tough problems in artificial intelligence are to be solved.  Recently, hybrid intelligent systems are becoming popular due to their capabilities in handling many real world complex problems, involving imprecision, uncertainty and vagueness, high-dimensionality. is A hybrid intelligent system is  one that combines at least two intelligent technologies. For example, Combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system. Combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system. Combining a neural network with a rough system results in a hybrid neuro-rough system. Etc. Combining a neural network with a rough system results in a hybrid neuro-rough system. Etc. The combination of probabilistic reasoning, fuzzy logic, neural networks and evolutionary computation forms the core of soft computing, an emerging approach to building hybrid intelligent systems capable of reasoning and learning in an uncertain and imprecise environment.

WRSTA, 13 August, 2006 Intelligent Systems Rough Sets Fuzzy Logic Neural Networks Evolutionary Algorithms Chaos & Fractals Belief Networks The primordial soup

WRSTA, 13 August, 2006 Fuzzy Logic : the algorithms for dealing with imprecision and uncertainty Neural Networks : the machinery for learning and function approximation with noise Evolutionary Algorithms : the algorithms for reinforced search and optimization RS Rough Sets uncertainty arising from the granularity in the domain of discourse Different methods = different roles

WRSTA, 13 August, 2006 Comparison of Expert Systems, Fuzzy Systems, Neural Networks and Genetic Algorithms

WRSTA, 13 August, 2006 Objective  Introduce a rough neural intelligent approach for: Rule generation and image classification. An application of breast cancer imaging has been chosen and hybridization of intelligent computing techniques has been applied to see their ability and accuracy to classify the breast cancer images into two outcomes:  malignant cancer or benign cancer.  Computer-based to assist radiologists in mammography classification of breast cancer images ( Computer Aided Diagnosis System )

WRSTA, 13 August, 2006 Mammogram Analysis Framework

WRSTA, 13 August, 2006 Mammogram Analysis Framework  Pre-processing phase – Fuzzy theory Enhancement Segmentation: Region of Interest (ROI) Region Boundary Enhancement  Feature Extraction phase Statistical features – concurrence Matrix  Rough Sets Data Analysis Feature representation – Rough information system Reduct generation Rule generation  Classification phase Rough neural classifier  Evaluation

WRSTA, 13 August, 2006 Pre-Processing – Fuzzy theory Mammograms are images that are difficult to interpret; therefore, techniques are needed to:  Enhance the quality of these images for a better interpretation.  For this purpose, a pre-processing phase of the images is adopted to improve the quality of the images and to make the feature extraction phase more reliable. It contains several processes;  to enhance the contrast of the whole image;  Fuzzy histogram hyperbolization algorithm (FHH)  to extract the region of interest;  Modified Fuzzy c-mean clustering algorithm  to enhance the edges surrounding the region of interest.  Fuzzy histogram hyperbolization algorithm (FHH)

WRSTA, 13 August, 2006 Feature Extraction Once the pre-processing was completed, features relevant to region of interest classification are extracted, normalized and represented in a database as vector values Gray level co-occurrence matrix (GLCM)  Energy, entropy, contrast and inverse difference moment.

WRSTA, 13 August, 2006 Rough Sets Data Analysis Create decision table Compute some reduct with minimal number of attributes. Significance of attributes: calculate the weight of the attributes. Rule Generation Rule Evaluation

WRSTA, 13 August, 2006 Rough neural network: rough neuron

WRSTA, 13 August, 2006 Results (Enhancement)

WRSTA, 13 August, 2006 Results (Segmentation)

WRSTA, 13 August, 2006

Average Execution time

WRSTA, 13 August, 2006 Number of generated rules and classification accuracy

WRSTA, 13 August, 2006 Conclusion Introducing a hybrid scheme that combines the advantages of different soft computing techniques for breast cancer detection.  Fuzzy sets is used as a pre-processing techniques to enhance the contrast of the whole image; to extracts the region of interest and then to enhance the edges surrounding the region of interest.  Then, subsequently extract features from the extracted regions characterizing the underlying texture of the interested regions.  Feature extractions acquired in this work are derived from the gray-level co- occurrence matrix.  A rough set approach to attribute reduction and rule generation has been used.  Rough neural networks were designed for discrimination for different regions of interest to test whether they are cancer or nun-cancer. The results proved that the soft computing techniques are very successful and has high detection accuracy.