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Lung Nodule Classification Using Scale-Invariant Neural Networks

thesis
posted on 2021-04-15, 07:46 authored by MUNDHER AL-SHABI
The size of a lung nodule is very diverse. The high variation of nodule sizes makes classifying them a difficult and challenging task. In this thesis, we propose four different Scale-Invariant Neural Networks for lung nodule classification. We examined our proposed methods on two different public datasets and compared their performance with state-of-the-art literature methods. The results show that the proposed model outperforms state-of-the-art methods and achieves an AUC of 98.05% and accuracy of 95.28% on the LIDC-IDRI dataset. These results will lead to identifying lung cancer in its early stages.

History

Campus location

Malaysia

Principal supervisor

Maxine

Additional supervisor 1

Liang Shiuan Ni

Year of Award

2021

Department, School or Centre

School of Engineering (Monash University Malaysia)

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Engineering

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