DEEP RQN: DEEP RESIDUAL QUANTUM NETWORK WITH VOCAL-BASED SUPPORT SYSTEM FOR LARYNGEAL CANCER CLASSIFICATION

Laryngeal cancer is a condition where the cancer cells form in the larynx tissues. Laryngeal cancer is caused by the use of tobacco and alcohol consumption, which are known to increase the risk of developing laryngeal cancer. The symptoms of laryngeal cancer are a persistent sore throat and ear pain. Several limitations are faced by previous techniques, including high diagnostic inconsistency, speech signal variability, limited real-time diagnosis, and the cost of technology. To overcome these limitations, a model named Deep Residual Quantum Network (Deep RQN) is developed for classifying laryngeal cancer. Initially, the input speech signal is subjected to signal preprocessing, which is performed using an adaptive Gaussian filter. Then, feature extraction is accomplished for extracting the features that include Zero Crossing Rate (ZCR), glottal waveform, Discrete Wavelet Transform (DWT), Mel Frequency Ceptral Coefficient (MFCC), and statistical features, including standard deviation, skewness, and kurtosis. Then, the extracted features are subjected to a Vocal Tract Support system to classify laryngeal cancer, which is done by Deep RQN, where the result is obtained as normal or disordered. When the result is considered normal, the corresponding signal is saved into the database, and if it is assumed to be the disordered signal, then it undergoes signal segmentation and speech enhancement. The signal segmentation is performed using Maximum A Posteriori Probability (MAP). Moreover, Natural Language Processing (NLP) based speech enhancement is done using the Gaussian Mixture Model (GMM), and its output results in a word sequence. Furthermore, the enhanced signal is applied to the database. The Deep RQN combines a Deep Quantum Neural Network (DQNN) and a Deep Residual Network (DRN). The Deep RQN attained superior outcomes than previous methods with metrics, including accuracy, sensitivity, and specificity of 91.55%, 91.90%, and 92.56%, respectively.