Hardware Plateforms Benchmark For Real-Time Polyp Detection
Résumé
In this article, our concern is the early diagnosis of colorectal cancer from a computer-aided detection point of view in order to help physicians in their diagnosis during the gold standard examination: optical videocolonoscopy. Since many years, some methods and materials have been developed to reduce the polyp missrate 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 small-connected object like videocapsule, a promising alternative to standard colonoscopy