Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149118
Title: Breast tumour segmentation using convolutional neural network on 3D computed tomography images
Authors: Ng, Yew Kong
Keywords: Engineering
Engineering::Mechanical engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Ng, Y. K. (2021). Breast tumour segmentation using convolutional neural network on 3D computed tomography images. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149118
Project: A089
Abstract: Image Segmentation of CT Images have always been costly in terms of time and money. The usual procedure includes having an experienced radiologist looking through the patient’s CT Images and manually segmenting out the cancer tumour. With the rise of computational power made available through advancements in technology, more and more healthcare practitioners are looking into using Artificial Intelligence (AI) solutions to help solve such problems. We are able to observe more and more AI in healthcare research being conducted, especially during the recent 2-3 years. As Deep Learning techniques in Computer Vision mature, it presents itself as a suitable candidate for use in medical diagnosis. Moreover, Deep Learning models have shown to even perform at radiologists’ level of performance at the segmentation task [35]. For this project, deep learning techniques will be applied to perform automatic tumour segmentation, which aims to reduce the manpower requirements as well as time needed for cancer diagnosis. We strive to accomplish this through the use of the U-Net architecture and its variants, which is widely cited in the medical image field for having relatively high accuracy & computational speed. We are able to achieve a Dice score of 0.8790 on the test set using U-Net with MobileNetV2 encoder on custom loss with data augmentation. Almost all models trained on custom loss are able to achieve a Dice score of above 0.8 which show great promise of using AI to aid in faster diagnosis.
URI: https://hdl.handle.net/10356/149118
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

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