ABSTRACT
Laryngeal cancer (LC) represents a substantial world health problem, with diminished survival rates attributed to late-stage diagnoses. Correct treatment for LC is complex, particularly in the final stages. This kind of cancer is a complex malignancy inside the head and neck region of patients. Recently, researchers serving medical consultants to recognize LC efficiently develop different analysis methods and tools. However, these existing tools and techniques have various problems regarding performance constraints, like lesser accuracy in detecting LC at the early stages, additional computational complexity, and colossal time utilization in patient screening. Deep learning (DL) approaches have been established that are effective in the recognition of LC. Therefore, this study develops an efficient LC Detection using the Chaotic Metaheuristics Integration with the DL (LCD-CMDL) technique. The LCD-CMDL technique mainly focuses on detecting and classifying LC utilizing throat region images. In the LCD-CMDL technique, the contrast enhancement process uses the CLAHE approach. For feature extraction, the LCD-CMDL technique applies the Squeeze-and-Excitation ResNet (SE-ResNet) model to learn the complex and intrinsic features from the image preprocessing. Moreover, the hyperparameter tuning of the SE-ResNet approach is performed using a chaotic adaptive sparrow search algorithm (CSSA). Finally, the extreme learning machine (ELM) model was applied to detect and classify the LC. The performance evaluation of the LCD-CMDL approach occurs utilizing a benchmark throat region image database. The experimental values implied the superior performance of the LCD-CMDL approach over recent state-of-the-art approaches.
ABSTRACT
OBJECTIVE: To record envelope following responses (EFRs) to monaural amplitude-modulated broadband noise carriers in which amplitude modulation (AM) depth was slowly changed over time and to compare these objective electrophysiological measures to subjective behavioral thresholds in young normal hearing and older subjects. PARTICIPANTS: three groups of subjects included a young normal-hearing group (YNH 18 to 28 years; pure-tone average = 5 dB HL), a first older group ("O1"; 41 to 62 years; pure-tone average = 19 dB HL), and a second older group ("O2"; 67 to 82 years; pure-tone average = 35 dB HL). Electrophysiology: In condition 1, the AM depth (41 Hz) of a white noise carrier, was continuously varied from 2% to 100% (5%/s). EFRs were analyzed as a function of the AM depth. In condition 2, auditory steady-state responses were recorded to fixed AM depths (100%, 75%, 50%, and 25%) at a rate of 41 Hz. Psychophysics: A 3 AFC (alternative forced choice) procedure was used to track the AM depth needed to detect AM at 41 Hz (AM detection). The minimum AM depth capable of eliciting a statistically detectable EFR was defined as the physiological AM detection threshold. RESULTS: Across all ages, the fixed AM depth auditory steady-state response and swept AM EFR yielded similar response amplitudes. Statistically significant correlations (r = 0.48) were observed between behavioral and physiological AM detection thresholds. Older subjects had slightly higher (not significant) behavioral AM detection thresholds than younger subjects. AM detection thresholds did not correlate with age. All groups showed a sigmoidal EFR amplitude versus AM depth function but the shape of the function differed across groups. The O2 group reached EFR amplitude plateau levels at lower modulation depths than the normal-hearing group and had a narrower neural dynamic range. In the young normal-hearing group, the EFR phase did not differ with AM depth, whereas in the older group, EFR phase showed a consistent decrease with increasing AM depth. The degree of phase change (or phase slope) was significantly correlated to the pure-tone threshold at 4 kHz. CONCLUSIONS: EFRs can be recorded using either the swept modulation depth or the discrete AM depth techniques. Sweep recordings may provide additional valuable information at suprathreshold intensities including the plateau level, slope, and dynamic range. Older subjects had a reduced neural dynamic range compared with younger subjects suggesting that aging affects the ability of the auditory system to encode subtle differences in the depth of AM. The phase-slope differences are likely related to differences in low and high-frequency contributions to EFR. The behavioral-physiological AM depth threshold relationship was significant but likely too weak to be clinically useful in the present individual subjects who did not suffer from apparent temporal processing deficits.
