Comparison of Deep Learning Network for Breast Tumor Segmentation from X-Ray

Abstract

Over 8% of women will be diagnosed with breast tumors (BT) in their lifetime. Tumors are formed by the uncontrollable development of tissues in a specific area of the body. They can be benign or malignant. The best survival rate can be expected with earlier screening and diagnosis of the tumor. To distinguish between benign and malignant tumors in x-ray images of the breast, segmentation of the tumor is a crucial first step. Screening mammography is an efficient method of detecting BT. As a result, the research presented two distinct deep learning models, termed SegNet and UNet architectures, to segment BT from mammograms. Datasets accessible to the public were utilized in the proposed system, specifically INbreast. Histogram equalization is used on datasets during preprocessing to improve the compressed areas and normalize the pixel dispersion. To avoid overfitting and boost the quantity of training data, augmentation techniques are employed. The metrics like the dice coefficient (DC) and the Intersection of Union (IoU) score are considered to evaluate the model. The metrics of the SegNets model are greater than the U-Net, as demonstrated by the experimental results. For the INbreast dataset, the SegNets achieve a maximum DC of 92.75% and an IoU score of 86.49%.