Deepnet-based surgical tools detection in laparoscopic videos

Recently, deep learning has revolutionized significant advances in image classification, especially in Medical image (MI) processing. Surgical Data Science (SDS) has been developed as a scientific research field that aims to improve the health status of patients. Laparoscopic videos possess a highly significant information source that is integrally present in minimally invasive surgeries. Recognizing surgical tools based on the videos has promoted greater interest because of their significance. In most existing research, single-tool detection is carried out, but multiple-tool recognition is not concentrated well. However, multiple-tool recognition poses numerous challenges, including diverse lighting conditions, the appearance of multiple instruments in different representations, tissue blood, etc. Also, the detection speed of learning methodology is very low because of inherent complexities and improper handling of huge amounts of data. The proposed research introduces a novel DeepNet-Tool for automatic multi-tool classification in laparoscopy videos to address these existing challenges. This paper focuses on solving the spatial-temporal issues in detecting Surgical Tools (STs). The proposed model is implemented in Python, and the overall accuracy is 97.36 % with the Cholec80 dataset, 98.67 % with the EndoVis dataset, 99.73 % on EndoVis and 98.67 % on LapGyn4, respectively. Experimental outcomes of the proposed DeepNet-Tool showed higher effectiveness compared with other deep learning methods on the ST classification task. Thus, the proposed model has revealed the potential for clinical use in accurate ST classification.