Breast Cancer Image Pre-Processing With Convolutional Neural Network For Detection and Classification

Abstract

Breast Cancer is one of the most common types of cancer. This research was conducted with the purpose of developing a Computer-Aided Diagnosis to detect breast cancers from mammogram images. The mammogram images were obtained from the INbreast Dataset and Husada Hospital in Jakarta. The program was developed with the usage of pre-processing which includes Median Filtering, Otsu thresholding, Truncation Normalization, and Contrast Limited Adaptive Histogram Equalization to manipulate the images and Convolutional Neural Network to classify the images into either mass or normal, or either benign or malignant. The pre-processing pipeline have provided enhanced images to be used to train and test the Convolutional Neural Network. The best model achieved reached an accuracy, precision and sensitivity of 94.1%, 100% and 85.7% in classifying the mammogram images into benign or malignant, and 88.3%, 92.6% and 83.3% in classifying the mammogram images into mass or normal. In conclusion, the algorithm was able to classify mammogram images and has provided results as high as other related researches.