Intelligent 3D Analysis for Detection and Classification of Breast Cancer

Abstract

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Breast cancer computer aided diagnosis (CAD) systems can provide such help and they are important and necessary for breast cancer control. Micro calcifications and masses are the two most important indicators of malignancy, and their automated detection is very valuable for early breast cancer diagnosis. Since masses are often indistinguishable from the surrounding parenchymal, automated mass detection and classification is even more challenging. This research presents algorithms for building a classification system or CAD, especially to obtain the different characteristics of mass and micro calcification using association technique based on classification. Starting with an individual-specific deformable of 3D breast model, this modelling framework will be useful for tracking visible tumors between mammogram images, as well as for registering breast images taken from different imaging modalities. From the results, the classifier developed able to perform well by successfully classifying the cancer and non-cancer (normal) images with the accuracy of 97%. Apart from that, by applying color map to the final results of segmentation provides a more interesting display of information and gives more direction to the purpose of image processing, which distinguishes between cancerous and non-cancerous tissues.

Classification Model for Breast Cancer Mammograms

“…The SVM classification model has outperformed NN and NB models in the study, and it shows that SVM is a good choice for determining the state of the breast at the early stage. As compared to the previously developed NN model reported by [2], the currently developed SVM model outperformed their NN model. Their study also not considering any other machine learning techniques but just focusing on NN model only.…”

“…Naive Bayes, Neural Network, and Deep Learning are among the most common machine learning techniques used in cancer detection [2,[8][9][10][11]. However, in this study, ten (10) experiments have been conducted using the three (3) machine learning techniques namely Naïve Bayes, Neural Network and Support Vector Machine and the accuracy of each model in providing correct prediction for each experiment was recorded as shown in Section 3.…”

“…It is a model made to imitate the function of learning that owned the human brain. As stated by [2], the Neural classifier developed able to perform well by successfully classifying cancer and non-cancer (normal) images with an accuracy of 97%. However, this study did not show any comparison between the developed NN classifier and other types of the classifier.…”