A GPU-based breast cancer detection system using Single Pass Fuzzy C-Means clustering algorithm

Breast cancer is the most lethal type of cancer affecting women in the world. To improve the life quality of women, an early detection of this malignancy is always promising because this cancer is one of the cancers that can be managed and treated easily if it is detected earlier. Mammography is the standard screening method to diagnose breast cancer. At these days, there are a lot of researches that have been done to improve this screening method by benefiting from the enormous growth of technology. Graphics Processing Unit (GPU) is a parallel processor that can divide the complex computations tasks into subtasks and run them concurrently. Many medical imaging modalities offloaded their processing to this processor to help in improving the speed of healthcare systems in order to diagnose the illnesses in real time. This research introduces an acceleration method for the segmentation of the mammography images based on the GPU. In order to provide a better detection for the cancerous tumor, we use a modified version of the most common algorithm for image segmentation, which is the Single Pass Fuzzy C-Means (FCM) algorithm. The approach will be applied to a set of mammogram images to distinguish between malignant and benign cases. Additionally, the system is implemented on GPU parallel processor as well as the traditional CPU in order to compare the performance of both implementations. The performance results are compared according to the execution time and the speedup metrics. The proposed implementation on GPU provides a fine speedup compared to its serial implementation on CPU.