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AI enabled surgery: enabling da Vinci Xi robot with live cancerous tumor detection

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posted on 2023-03-02, 01:10 authored by Rohan Ibn Azad

Deep learning has proved successful in Computer Aided Detection in interpreting ultrasound images, CT scans, identifying COVID infections, identifying tumors from ultrasound, Computed Tomography (CT) scans for humans and for animals. Currently, only experienced surgeons can identify tumors in patients with kidney cancer using ultrasound and Indocyanine Green (ICG) with Fluorescence Imaging which may come with error. Therefore, this project proposes applications of deep learning in detecting cancerous tissue inside patients via laparoscopic camera on da Vinci Xi surgical robots. The proposed algorithm can help the surgeons to detect cancerous tumors from fatty tissue and non-cancerous tissue with 84% accuracy during the surgery which is extremely beneficial to ensure all the cancerous tumors are removed. The process is carried out via object detection techniques which draws bounding boxes and shows the probability for that region to be cancerous tissue, non cancerous tissue or fatty tissue, which is the primary goal of the project. The project compares between optimized AlexNet, VGG-16, YOLOv3, YOLOv4 to work out the best algorithm with tuned hyperparameter to detect cancerous tissue during surgery. Analysing images, the final mAP for object detection was 0.974 and for classification, the accuracy was 0.84.

History

Table of Contents

1. Introduction -- 2. Literature review -- 3. Methodology -- 4. Performance analysis -- 5. Conclusion and future work

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Department, Centre or School

School of Engineering

Year of Award

2022

Principal Supervisor

Mohsen Asadnia

Additional Supervisor 1

Subhas Mukhopadhyay

Rights

Copyright: The Author Copyright disclaimer: https://www.mq.edu.au/copyright-disclaimer

Language

English

Extent

60 pages

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