A Bidirectional Multi-Layer independently RNN (BML-indRNN) model is proposed in this paper . spatial feature extraction is implemented via fine-tuning of a Deep Convolutional Neural Network . Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to eliminate the black-box effects of DCNN . Results indicated that the testing accuracy for the suturing task based on our proposed method is 87.13%, which outperforms most of the state-of-the-art algorithms. It can provide explainable results by showing the regions of the surgical images that have a strong relationship with the surgical gesture classification results. The proposed method was evaluated based on the suture task with data obtained from the public available JIGSAWS database. Comparative studies were conducted to verify the proposed framework

Author(s) : Dandan Zhang, Ruoxi Wang, Benny Lo

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Code :
Coursera

Keywords : proposed - results - surgical - based - feature -

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