Lesion Segmentation from Mammogram Images using a U-Net Deep Learning Network
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IJERTV9IS020213
Lesion Segmentation from Mammogram Images using a U-Net Deep Learning Network
Neha S. Todewale
Breast cancer is one of the most frequent mortality causes among women. In order to get the proper results and for early diagnosis, the efforts are being taken for developing more effective technique to improve the results. Previous methods need radiologist or oncologist to examine the presence of cancer and consume a lot of time. Thus, in order to diagnose the tumor or the cancerous cells without the involvement of human but with good accuracies, different approaches need to be used. In this study, deep learning approach is used to automatically segment the cancerous lesion. For the segmentation, U-Net model, which is a Fully Convolutional Neural network (FCN), is used. The database used are TMC (Tata Memorial Centre) data and MIAS (Mammographic Imaging Analysis Society). This computer-aided detection helps to improve the results by properly separating out the lesion which will help to study the temporal changes taking place within lesion over the time. The network is found to give an acceptable performance with the given datasets. The validation dice coefficients of the MIAS and TMC datasets are found to be 0.8582 and 0.8952 respectively.
