Intelligent throat polyp detection with separable compressive sensing

Compressive sensing can minimize the collection of redundant data in the acquisition step. However, it requires a huge amount of storage and creates a tremendous computation burden due to the size of random measurement matrix in compressive sensing theory for big data collection. The separable compressive sensing theory uses two-dimensional separable random measurement matrixes instead of a huge size of random matrix to remedy sensing matrix storage and computation complexity. In this paper, we proposed an intelligent throat polyp detection with singular value decomposition and support vector machine algorithms based on the vowel/a:/and/i:/voices. We compared the detection effects of the proposed intelligent detection method between original voice signals and compressed signals which were collected by separable compressive sensing theory. The experimental results showed that the matrix size of original vowel voices signal could affect the correct rate of prediction. Also, the correct rate of prediction was stable under different random measurement matrix and different compressed ratio.