Hardware Platforms Benchmark For Real-Time Polyp Detection

In this article, our concern is the early diagnosis of colorectal cancer from a computeraided detection point of view in order to help physicians in their diagnosis during the gold standard examination: optical video colonoscopy. Since many years, some methods and materials have been developed to reduce the polyp miss rate and to improve detection capabilities. Nevertheless, the real challenge lies in the real-time use of these methods. In this context, more precisely, we focus our attention on the hardware implementation of a previous method we recently introduced in the literature for real-time detection of colorectal polyps, lesions that may degenerate into cancer. This implementation is subject to three performance criteria: real-time processing capabilities, detection rate and necessary computational resources. Six different platforms were tested and compared. If we noticed that only workstation computers are able to perform the detection with a good tradeoff between the three aforementioned criteria, possibilities of architecture optimizations are also identified and discussed in order to achieve real-time performance on platforms with low available computational resources like Raspberry Pi for instance. This latter issue is of major importance for possible integration of the detection algorithm inside smallconnected object like videocapsule, a promising alternative to standard colonoscopy.