Comparison of Multiple Deep Models on Semantic Segmentation for Breast Tumor Detection

Abstract

The early diagnosis of breast tumor detection is the most significant research issue in mammography. Computer-aided diagnosis (CAD) is one of the highly essential methods to prevent breast cancer. This research work explored the effectiveness of deep-based pixel-wise segmentation models for low energy X-rays (mammographic imagery) to detect tumors in the breast region. For this purpose, various semantic segmentation models were incorporated into the experimental procedure. All the models were analyzed using the medical images dataset, which was gathered and annotated from one of the largest teaching hospitals in the Khyber Pakhtunkhwa province, known as Lady reading hospital. It is coordinated in cooperation with local health specialists, radiologists, and technologists. The comparative analysis of the incorporated segmentation techniques' performance was observed, selecting the most appropriate model for detecting tumors and normal breast regions. The experimental evaluation of the proposed models performs efficient detection of tumor and non-tumor areas in breast mammograms using traditional evaluation metrics such as mean IoU and Pixel accuracy. The performance of the semantic segmentation techniques was evaluated on two datasets (Cityscapes and mammogram). Dilation 10 (global) performed the best among the four semantic segmentation models by achieving a higher pixel accuracy of 93.69%. It reflects the effectiveness of the pixel-wise segmentation techniques by outperforming other state-of-the-art automatic image segmentation models.