Multi-scale GC-T2: Automated region of interest assisted skin cancer detection using multi-scale graph convolution and tri-movement based attention mechanism

Alqarafi, Abdulrahman; Ahmad Khan, Arfat; Kumar Mahendran, Rakesh; Al-Sarem, Mohammed; Albalwy, Faisal

doi:10.1016/j.bspc.2024.106313

Biomedical Signal Processing and Control

2024

DOI: 10.1016/j.bspc.2024.106313

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Multi-scale GC-T2: Automated region of interest assisted skin cancer detection using multi-scale graph convolution and tri-movement based attention mechanism

Abdulrahman Alqarafi,

Arfat Ahmad Khan,

Rakesh Kumar Mahendran

et al.

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2024

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Cited by 3 publications

References 27 publications

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S2SNet: Two-stream geometry-aware sequence to sequence network for robot motion skills learning and generalization

Xu,

Qian,

Zhou

et al. 2024

Advanced Engineering Informatics

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S2SNet: Two-stream geometry-aware sequence to sequence network for robot motion skills learning and generalization

Xu,

Qian,

Zhou

et al. 2024

Advanced Engineering Informatics

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Distance-based integration method for human skin type identification

Srimaharaj,

Chaising

2024

Computers in Biology and Medicine

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Helicopter cockpit speech recognition method based on transfer learning and context biasing

Wang,

Wang

et al. 2024

Eng. Res. Express

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Currently, Chinese speech recognition technology is generally designed for common domains, primarily focusing on accurate recognition of standard Mandarin Chinese in low-noise environments. However, helicopter cockpit speech presents unique challenges, characterized by high-noise environments, specific industry jargon, low contextual relevance, and a lack of publicly available datasets. To address these issues, this paper proposes a helicopter cockpit speech recognition method based on transfer learning and context biasing. By fine-tuning a general speech recognition model, we aim to better adapt it to the characteristics of speech in helicopter cockpits. This study explores noise reduction processing, context biasing, and speed perturbation in helicopter cockpit speech data. Combining pre-trained models with language models, we conduct transfer training to develop a specialized model for helicopter cockpit speech recognition. Finally, the effectiveness of this method is validated using a real dataset. Experimental results show that, on the helicopter speech dataset, this method reduces the word error rate from 72.69% to 12.58%. Furthermore, this approach provides an effective solution for small-sample speech recognition, enhancing model performance on limited datasets.

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Multi-scale GC-T2: Automated region of interest assisted skin cancer detection using multi-scale graph convolution and tri-movement based attention mechanism

Cited by 3 publications

References 27 publications

S2SNet: Two-stream geometry-aware sequence to sequence network for robot motion skills learning and generalization

S2SNet: Two-stream geometry-aware sequence to sequence network for robot motion skills learning and generalization

Distance-based integration method for human skin type identification

Helicopter cockpit speech recognition method based on transfer learning and context biasing

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