Breast Cancer Detection Using Convolutional Neural Networks
This work addresses the problem of manual, subjective breast cancer diagnosis in Ethiopia, offering an automated solution for medical image analysis, though it is incremental as it adapts existing methods to a specific domain.
The study tackled breast cancer detection in mammogram images by proposing a Convolutional Neural Network (CNN) model that integrates Region Proposal Network (RPN) and Region of Interest (ROI) from faster R-CNN, achieving a detection accuracy of 91.86%, sensitivity of 94.67%, and AUC-ROC of 92.2%.
Breast cancer is prevalent in Ethiopia that accounts 34% among women cancer patients. The diagnosis technique in Ethiopia is manual which was proven to be tedious, subjective, and challenging. Deep learning techniques are revolutionizing the field of medical image analysis and hence in this study, we proposed Convolutional Neural Networks (CNNs) for breast mass detection so as to minimize the overheads of manual analysis. CNN architecture is designed for the feature extraction stage and adapted both the Region Proposal Network (RPN) and Region of Interest (ROI) portion of the faster R-CNN for the automated breast mass abnormality detection. Our model detects mass region and classifies them into benign or malignant abnormality in mammogram(MG) images at once. For the proposed model, MG images were collected from different hospitals, locally.The images were passed through different preprocessing stages such as gaussian filter, median filter, bilateral filters and extracted the region of the breast from the background of the MG image. The performance of the model on test dataset is found to be: detection accuracy 91.86%, sensitivity of 94.67% and AUC-ROC of 92.2%.