Artificial Neural Networks in UWB Image Processing for Early Detection of Breast Cancer

Abstract

The medical imaging methods have been commonly used for breast cancer diagnosis and detection. The limitation of using these techniques is the vast amount of time a qualified radiologist takes in the manual analysis of image pattern. Automated classifiers may dramatically upgrade the diagnostic process by automatically separating benign and malignant patterns in terms of both accuracy and time requirements. Artificial Neural network (ANN) plays an important role in this respect, especially in the application of UWB image processing for early breast cancer detection. It is effectively detected by using these steps. Pre-processing, Image partitioning, attribute extraction and categorization. Gaussian and median filter is used for performing pre-processing operation. It helps to clear out the noise and it transforms the RGB image into gray scale image. Threshold based segmentation is used for segmenting the image. For performing feature extraction, GLCM technique is used and optimized using ANN. By using GLCM, 12 feature derivatives were extracted. Extraction is done based on colour, texture and shape. Itplays a major role in detection. Eventually, for the classification of the extracted function, Artificial Neural Network is executed and then it analogizes the test data with the trained data. To prove its usefulness, it produces a high accuracy compared with other current plays. The accuracy of this suggested analysis is 90%.

 Keywords:Ultra-wideband (UWB), Median filter, Threshold based segmentation, Artificial Neural Network (ANN),Gray level Co-occurrence matrix (GLCM).