Ihsan, MA, Eram, AF, Nahar, L and Kadir, MA (2024) MediSign: An Attention-Based CNN-BiLSTM Approach of Classifying Word Level Signs for Patient-Doctor Interaction in Hearing Impaired Community. IEEE Access, 12. pp. 33803-33815.
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MediSign An Attention-Based CNN-BiLSTM Approach of Classifying Word Level Signs for Patient-Doctor Interaction in Hearing Impaired Community.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview |
Abstract
Along with day-to-day communication, receiving medical care is quite challenging for the hearing impaired and mute population, especially in developing countries where medical facilities are not as modernized as in the West. A word-level sign language interpretation system that is aimed toward detecting medically relevant signs can allow smooth communication between doctors and hearing impaired patients, ensuring seamless medical care. To that end, a dataset from twenty distinct signers of diverse backgrounds performing 30 frequently used words in patient-doctor interaction was created. The proposed system has been built employing MobileNetV2 in conjunction with an attention-based Bidirectional LSTM network to achieve robust classification, where the validation accuracy and f1-scores were 95.83% and 93%, respectively. Notably, the accuracy of the proposed model surpasses the recent word-level sign language classification method in a medical context by 5%. Furthermore, the comparison of evaluation metrics with contemporary word-level sign language recognition models in American, Arabic, and German Sign Language further affirmed the capability of the proposed architecture.
Item Type: | Article |
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Uncontrolled Keywords: | Sign language; Assistive technologies; Hidden Markov models; Medical services; Error analysis; Patient monitoring; Deafness; Convolutional neural networks; Long short term memory; Bidirectional control; Attention; BiLSTM; MobileNetV2; patient-doctor interaction; sign language; 08 Information and Computing Sciences; 09 Engineering; 10 Technology |
Subjects: | Q Science > QH Natural history > QH301 Biology R Medicine > R Medicine (General) R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine |
Divisions: | Pharmacy and Biomolecular Sciences |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
SWORD Depositor: | A Symplectic |
Date Deposited: | 30 Oct 2024 14:13 |
Last Modified: | 30 Oct 2024 14:15 |
DOI or ID number: | 10.1109/ACCESS.2024.3370684 |
URI: | https://researchonline.ljmu.ac.uk/id/eprint/24627 |
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