Enhanced EfficientNet Network for Classifying Laparoscopy Videos using Transfer Learning Technique

Recent days have seen a lot of interest in surgical data science (SDS) methods and imaging technologies. As a result of these developments, surgeons may execute less invasive procedures. Using pathology and no pathology situations to classify laparoscopic video pictures of surgical activities, in this research work authors conducted their investigation using a transfer learning technique named enhanced ENet (eENet) network based on enhanced EfficientNet network. Two base versions of the EfficientNet model named ENetB0 and ENetB7 along with the two proposed versions of the EfficientNet network as enhanced EfficientNetB0 (eENetB0) and enhanced EfficientnetB7 (eENetB7) are implemented in the proposed framework using publicly available GLENDA [1] dataset. The proposed eENetB0 and eENetB7 models have classified the features extracted using the transfer learning technique into binary classification. For 70–30 and 10-fold Cross-Validation (10-fold CV), the data splitting eENetB0 model has achieved maximum classification accuracy as 88.43% and 97.59%, and the eENetB7 model has achieved 97.72% and 98.78% accuracy. We also compared the performance of our proposed enhanced version of EfficientNet (eENetB0 and eENetB7) with the base version of the models (ENetB0 and ENetB7) it shows that among these four models eENetB7 performed well. For GUI-based visualization purposes, we also created a platform named IAS.ai that detects the surgical video clips having blood and dry scenarios and uses explainable AI for unboxing the deep learning model's performance. IAS.ai is a real-time application of our approach. For further validation, we compared our framework's performance with other leading approaches cited in the literature [2]–[4]. We can see how well the proposed eENet model does compare to existing models, as well as the current best practices.