Fine Needle Aspiration Cytology Evaluation for Classifying Breast Cancer Using Artificial Neural Network
Nor Ashidi Mat Isa, Esugasini Subramani, Mohd Yusoff Mashor, Nor Hayati Othman · American Journal of Applied Sciences · 2007
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 pre-cancerous 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