Voice-Disorder Identification of Laryngeal Cancer Patients

Abstract

This Previous studies have shown that much of laryngeal cancer-based work was carried out with a minimal set of linear features. Much of the work was focused on the study of larynx preservation, quality of life around radiotherapy, or surgery. The voice disorder database was not solely limited to laryngeal cancer. In the context of this, the paper proposes a noninvasive voice disorder detection of laryngeal cancer patients. The sustained vowel /a/ was recorded with 55 laryngeal cases and 55 healthy cases. Owing to the non-linearity property of the vocal cords, seven non-linear parameters along with biologically inspired 39 Mel-Frequency Cepstral Coefficients (MFCC) are extracted. This forms a laryngeal dataset of size 110X46. The wrapper method is used for better feature selection and to enhance the discriminating ability of the present work. The classification is carried out using a tuned support vector machine (SVM) with grid search and random forest (RF). The present work has shown an improved accuracy of 76.56% with SVM and 80% in the case of random forest. The forward selection of features along with the involvement of non-linear features has played a significant role in the better performance of the present system.