Neural network architectures in data sequence analysis: diagnosis of Reinke's oedema and Laryngeal polyps

• Autorzy: Szkoła, Jarosław
• Języki publikacji: EN

Abstrakty

EN

In this review paper, we focus our attention on presenting selected neural network architectures dedicated to the analysis of sequential data, in particular to support the diagnosis of Reinke’s oedema and laryngeal polyps. The research discussed here is located in the area of clinical computer decision support systems (CDS) based on the use of artificial recurrent neural networks (RNNs) for speech signal analysis. RNNs are able to predict time series due to their memory and local recurrent connections. In the experimental part, Elman-Jordan artificial neural networks are used, whose characteristics are speed and accuracy in pattern learning allowing real-time decision-making. In the review presented here, one important theme is the use of Bezier curves for preprocessing the speech signal, leading to data reduction and noise elimination. Elman-Jordan networks significantly speed up the learning process and show high classification accuracy in laryngopathy diagnosis.

Słowa kluczowe

EN

clinical decision support; recurrent neural network; Reinke’s oedema; laryngeal polyps; Bézier curves; speech signal analysis; temporal patterns; classification

• Czasopismo: Technical Sciences / University of Warmia and Mazury in Olsztyn
• Rocznik: 2024
• Tom: Vol. 27
• Strony: 113--125
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Szkoła, Jarosław

• Instytut Informatyki, Zakład Metod Przybliżonych, Uniwersytet Rzeszowski, ul. Pigonia 1, 35-310 Rzeszów,

Bibliografia

• BAKATOR M., RADOSAV D. 2018. Deep Learning and Medical Diagnosis: A Review of Literature. Multimodal Technologies and Interaction, 2(3):47. https://doi.org/10.3390/mti2030047
• ELMAN J. 1993. Distributed Representations, Simple Recurrent Networks, and Grammatical Structure. Machine Learning, 7: 195-225. https://doi.org/10.1007/BF00114844
• JORDAN M.I. 1986. Serial Order: A Parallel Distributed Processing Approach. ICS Report 8604. Institute for Cognitive Science University of California, San Diego.
• MEHRISH A., MAJUMDER N., BHARADWAJ R., MIHALCEA R., PORIA S. 2023. A Review of Deep Learning Techniques for Speech Processing. Information Fusion, 99: 101869. https://doi.org/10.1016/j.inffus.2023.101869
• SFAYYIH A.H., SABRY A.H., JAMEEL S.M., SULAIMAN N., RAAFAT S.M., HUMAIDI A.J., KUBAIAISI Y.M.A. 2023a. Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview. Diagnostics, 13(10):1748. https://doi.org/10.3390/diagnostics13101748
• SFAYYIH A.H., SULAIMAN N., SABRY A.H. 2023b. A Review on Lung Disease Recognition by Acoustic Signal Analysis with Deep Learning Networks. Journal of Big Data, 10(1), Article number 101. https://doi.org/10.1186/s40537-023-00762-z
• SZKOLA J., PANCERZ K., WARCHOL J. 2010a. Computer Diagnosis of Laryngopathies Based on Temporal Pattern Recognition in Speech Signal. Bio-Algorithms and Med-Systems, 6(12): 75-80.
• SZKOŁA J., PANCERZ K., WARCHOŁ J. 2010b. Computer-Based Clinical Decision Support for Laryngopathies Using Recurrent Neural Networks. 10th International Conference on Intelligent Systems Design and Applications, Cairo, Egypt, p. 627-632. https://doi.org/10.1109/ISDA.2010.5687195
• SZKOŁA J., PANCERZ K., WARCHOŁ J. 2011a. Improving Learning Ability of Recurrent Neural Networks: Experiments on Speech Signals of Patients with Laryngopathies. In: F. Babiloni, A. Fred, J. Filipe, H. Gamboa (Eds.) Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS ‘11). SciTePress – Science and Technology Publications, Setúbal, p. 360-364. https://doi.org/10.5220/0003292603600364
• SZKOŁA J., PANCERZ K., WARCHOŁ J. 2011b. A Bezier Curve Approximation of the Speech Signal in the Classification Process of Laryngopathies. Federated Conference on Computer Science and Information Systems (FedCSIS), Szczecin, p. 141-146. Retrieved from https://annals-csis.org/proceedings/2011/pliks/221.pdf
• TANVEER M., RASTOGI A., PALIWAL V., GANAIE M.A., MALIK A.K., DEL SER J., LIN C.-T. 2023. Ensemble Deep Learning in Speech Signal Tasks: A Review. Neurocomputing, 550: 126436. https://doi.org/10.1016/J.NEUCOM.2023.126436
• ZAIDI B., SELOUANI S., BOUDRAA M., SIDI YAKOUB M. 2021. Deep Neural Network Architectures for Dysarthric Speech Analysis and Recognition. Neural Computing and Applications, 33: 9089-9108.

Identyfikatory

DOI

10.31648/ts.10228