Diagnosis of Breast Cancer from Mammogram Images Based on CNN

Lin Dong,Kohei Inoue

doi:10.12792/jiiae.8.117

Abstract

Breast cancer has become the most common malignant tumor with the highest incidence of death in women. The MIBCAD (Medical Image Based Computer-Aided Diagnosis) system currently in use has a low diagnostic accuracy rate of only 85%. Furthermore, this system has major limitations for image processing of mammogram. To address these issues, this paper proposed a breast cancer diagnosis method based on an improved CNN (Convolutional Neural Networks). To avoid the image overfitting problem, transfer learning and data augmentation methods were used. The image classification accuracy was improved by using different CNN structures and changing the classifier type. Our results showed that the classification accuracy of the model reached 91.4%, which was significantly improved compared with the existing MIBCAD system.

Highlights

According to the Japan National Cancer Center, breast cancer is the most common cancer among women and the fifth most common cause of death from cancer
Doctors use the images of the breast to determine if a lesion is present. The accuracy of this method of diagnosis depends on the doctor’s prior experience, and due to the differences in the level of diagnosis and prior experience between doctors, misdiagnosis and omission can occur. Another major reason for misdiagnosis is the fatigue of the doctor who has to read the mammography for a long time, which affects his or her own judgment
Method we propose a CNN model for breast cancer diagnosis. 2.1 Convolutional Neural Networks Recently, CNN have become a hot topic in the research field because of the high accuracy achieved in the field of image recognition, which is why we chose CNN

Summary

Introduction

According to the Japan National Cancer Center, breast cancer is the most common cancer among women and the fifth most common cause of death from cancer. Mammography is the most common method of performing early screening for breast cancer. This method is inexpensive and causes less pain to the patient and clearly shows the breast tissue structure. The accuracy of this method of diagnosis depends on the doctor’s prior experience, and due to the differences in the level of diagnosis and prior experience between doctors, misdiagnosis and omission can occur. Another major reason for misdiagnosis is the fatigue of the doctor who has to read the mammography for a long time, which affects his or her own judgment. Recent studies have shown that MIBCAD (Medical Image Based Computer-Aided Diagnosis) system is widely used to detect and diagnose breast cancer to improve doctors’ efficiency

Methods

Results

Discussion

Conclusion