Classification of pathological disorders using optimization enabled deep neuro fuzzy network

Abstract

The discovery of voice pathology is an emerging domain in biomedical engineering. The assessment helps to offer information for effective diagnosis. An optimization-enabled deep model for pathological disorder classification is devised in this paper. The speech signal is employed as the input of the pre-processor which subsequently helps to eliminate noise considering the Hanning window. The initial improvement is done by exploiting multi-band spectral subtraction for generating frames. The enhancement of speech is performed using Deep Residual Network (DRN) classifier. The training of DRN is performed by exploiting adopted Feedback Artificial Tree Firefly algorithm (FATFA) that is attained by combining Feedback Artificial Tree Algorithm (FAT), and Firefly algorithm (FA). The features, like Amplitude modulation spectrogram (AMS), and frequency-domain features namely tonal power ratio, pitch chroma, spectral spread, spectral flux, as well as spectral autocorrelation is mined from the improved speech. The classification is done with Deep Neuro-Fuzzy Network (DNFN), which is tuned with the devised Verse FATFA. The proposed Verse FATFA is conceived by merging FATFA and Multiverse Optimizer (MVO). Therefore, the devised Verse FATFA-based DNFN categories voice disorders into Laryngitis, Dysphonie, spasmodischedysphonie, Leukoplakie, Cyste, Dish-Syndrom, and Monochorditis. The proposed Verse FATFA-based DNFN provides enhanced performance with maximum values of 94.93% accuracy, 95.94% sensitivity with 91.93% specificity.