Computer Aided Detection and Classification of mammograms using Convolutional Neural Network

TL;DR

This paper presents a CNN-based method for classifying mammograms as normal or abnormal to aid early breast cancer detection, utilizing the DDSM dataset for improved diagnosis accuracy.

Contribution

The study introduces a CNN approach specifically designed for mammogram classification, addressing challenges in tumor shape and size variability.

Findings

CNN achieved high accuracy in classifying mammograms.

The method effectively distinguishes normal from abnormal tissues.

Results demonstrate potential for aiding early breast cancer diagnosis.

Abstract

Breast cancer is one of the most major causes of death among women, after lung cancer. Breast cancer detection advancements can increase the survival rate of patients through earlier detection. Breast cancer that can be detected by using mammographic imaging is now considered crucial step for computer aided systems. Researchers have explained many techniques for the automatic detection of initial tumors. The early breast cancer symptoms include masses and micro-calcifications. Because there is the variation in the tumor shape, size and position it is difficult to extract abnormal region from normal tissues. So, machine learning can help medical professionals make more accurate diagnoses of the disease whereas deep learning or neural networks are one of the methods that can be used to distinguish regular and irregular breast identification. In this study the extraction method for the classification of breast masses as normal and abnormal we have used is convolutional neural network (CNN) on mammograms. DDSM dataset has been used in which nearly 460 images are of normal and 920 of abnormal breasts.