  Minimally invasive surgery mainly consists of a series of sub-tasks, which
can be decomposed into basic gestures or contexts. As a prerequisite of
autonomic operation, surgical gesture recognition can assist motion planning
and decision-making, and build up context-aware knowledge to improve the
surgical robot control quality. In this work, we aim to develop an effective
surgical gesture recognition approach with an explainable feature extraction
process. A Bidirectional Multi-Layer independently RNN (BML-indRNN) model is
proposed in this paper, while spatial feature extraction is implemented via
fine-tuning of a Deep Convolutional Neural Network(DCNN) model constructed
based on the VGG architecture. To eliminate the black-box effects of DCNN,
Gradient-weighted Class Activation Mapping (Grad-CAM) is employed. 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 suturing task with data obtained
from the public available JIGSAWS database. Comparative studies were conducted
to verify the proposed framework. 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.
