Throat Polyp Detection Based on the Neural Network Classification Algorithm

This paper realizes the judgment that whether patients have throat polyp by normalization processing, principal component analyzing and Neural Network Classifying the extracted audio data. This implementation replaces the traditional approach to diagnosis of throat polyps. Conventional laryngoscopy need to cutout, clamp or puncture from the patient to remove the lesions to do pathological examinations, which is so hurt to the patient. The test for throat polyp prediction with the neural network classification algorithm are carried out. The results shows that the correct rate of prediction is stable under different number of samples and different random measurement matrices.

[1]  Haipeng Yao,et al.  Analysis speech of polypus patients based on channel parameters and fuzzy logic systems , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[2]  Jiasong Mu,et al.  Throat polyp detection based on compressed big data of voice with support vector machine algorithm , 2014, EURASIP Journal on Advances in Signal Processing.

[3]  Rishabh Kadyan,et al.  AN OVERVIEW OF DATA MINING , 2012 .

[4]  Kwang Suk Park,et al.  New method in acoustic analysis for the diagnosis of the laryngeal functions , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[5]  Moe Z. Win,et al.  Threshold-Based Time-of-Arrival Estimators in UWB Dense Multipath Channels , 2008, IEEE Transactions on Communications.

[6]  Svetha Venkatesh,et al.  Effective Anomaly Detection in Sensor Networks Data Streams , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[7]  Qilian Liang,et al.  Throat Polyps Detection Based on Patient Voices , 2012, ICC 2012.

[8]  Svetha Venkatesh,et al.  Anomaly detection in large-scale data stream networks , 2012, Data Mining and Knowledge Discovery.

[9]  Ying Wang,et al.  Intelligent throat polyp detection with separable compressive sensing , 2014, EURASIP Journal on Advances in Signal Processing.