Noninvasive Detection of Potentially Precancerous Lesions of Vocal Fold Based on Glottal Wave Signal and SVM Approaches

Purpose: Preventive detection of premalignant lesions of vocal fold can play an important role for earlier detection of larynx cancer. In this paper, an automatic detection of premalignant lesions based on human voice production theory is designed. More specifically, premalignant lesions such as leukoplakia, Erythroplakia, Keratosis, etc are directly related to the vocal fold, features extracted from glottal fold waveform can be pertinent and critical. The proposed method performs as non-intrusive technique based on the extraction of vocal fold waveform from the recorded utterances. Method: The basic idea is to extract pertinent features from the source signal (glottal waveform). However, such signal is not directly accessible. Hence, we perform a first treatment of recorded speech using Iterative Adaptive Inverse Filtering (IAIF) to extract the glottal waveform. IAIF is applied on two databases: Massachusetts Eye and Ear Inrmary (MEEI) database and Saarbrucken Voice Database (SVD) with sustained vowel samples. From the glottal flow impulses, pertinent instances are used to build pertinent features. A deep exploration of extracted features, using statistical tools such as boxplot and Principal Component Analysis (PCA), leads to select the most critical and pertinent features. By using Support Vector Machine Technique as classification technique, a discrimination between normal and premalignant tumor is achieved. Results: Preliminary results show that some features are not discriminant. Those features are discarded and only pertinent ones are used in learning and testing processing using SVM. The performances are assessed using four criteria, sensitivity, specificity, precision and accuracy. When selected features are combined according to PCA analysis, an accuracy of about 92% is accomplished. Conclusion: It is shown in this study that is possible to detect the premalignant lesions with acceptable and fairly sensitivity, specificity, precision and accuracy. Such algorithm can help detect at earlier stage laryngeal cancer.