Contribution of Data Augmentation for the Prenventive Detection of Vocal Fold Precancerous Lesions

Purpose: In this paper, an automatic detection of premalignant lesions based on human voice production theory is designed. The current framework is interested in particularly to the case of vocal fold precancerous lesion detection. Since the earlier detection of cancer is an important fact and directly related to medical treatment, the current paper presents a non-invasive and low time consuming technique for earlier cancer detection. Method: By a simple microphone, a speech signal can be picked up and analysed. We aim to extract the voice source signal from the acoustic speech signal. The voice source generated by vocal fold is altered when a premalignant lesion occurs. Features extracted from source voices are deeply analysed. However, due to the lack of speech samples, extracted features are augmented based on features analysis. Results: The adopted method based on boxplot, histogram and probability density leads to data augmentation of the extracted features. Augmented features are used in learning and testing, processing using SVM. The performances are assessed using four criteria, sensitivity, specificity, precision and accuracy. When augmented features are combined according to PCA analysis, an accuracy of premalignant lesion identification about 95% 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. The performances are improved when data augmentation process is used.