Fine Needle Aspiration Cytology Evaluation for Classifying Breast Cancer Using Artificial Neural Network

Abstract

Thirteen cytology of fine needle aspiration image (i.e. cellularity, background information, cohesiveness, significant stromal component, clump thickness, nuclear membrane, bare nuclei, normal nuclei, mitosis, nucleus stain, uniformity of cell, fragility and number of cells in cluster) are evaluated their possibility to be used as input data for artificial neural network in order to classify the breast precancerous cases into four stages, namely malignant, fibroadenoma, fibrocystic disease, and other benign diseases. A total of 1300 reported breast pre-cancerous cases which was collected from Penang General Hospital and Hospital Universiti Sains Malaysia, Kelantan, Malaysia was used to train and test the artificial neural networks. The diagnosis system which was developed using the Hybrid Multilayered Perceptron and trained using Modified Recursive Prediction Error produced excellent diagnosis performance with 100% accuracy, 100% sensitivity and 100% specificity.