Complex wavelet as nucleus descriptors for automated cancer cytology classifier system using ANN

Breast cancer is the most frequent cancer and the most frequent cause of cancer induced death in women in the world. Diagnosis and prognosis of this cancer can be done through the radiological, surgical, and pathologic assessments of breast tissue samples. In developing countries, testing for detection of this cancer involves visual microscopic test of cytology samples such as Fine Needle Aspiration Cytology (FNAC) taken from the patient's breast. The result of analysis on this sample by cyto-pathologist is crucial for breast cancer patient. In this paper, nucleus clusters of cells in the sub-band images of FNAC samples are investigated after decomposition by means of the complex discrete wavelet transform. From this a novel scheme is developed to compute the wavelet features based on the first and second order textural information of each color band. The ability of properly trained artificial neural networks to correctly classify and recognize patterns makes them particularly suitable for use in an expert system that aids in the diagnosis of cancer cytology images. Hence the extracted features are fed in to the artificial neural network as input for its classification task. The overall accuracy of classification of the proposed approach is 82.21%. The results of the analysis are found to be better than the previous study.