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Breast image classification using machine learning techniques

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thesis
posted on 28.03.2022, 20:33 by Aaron Mikaelian
Proper diagnosis of breast images may be performed through a combination of image processing and machine learning techniques. Provided an accurate diagnosis, the time in which a doctor would personally analyse the images is reduced. Practically, speaking, the diagnosis process consists of feature detection and classification. This research project investigates the performance of Tamura's texture analysis in combination with support vector machines (SVM) as a means to carry out this process. As the provided data consists of three classes, normal, benign and cancer, multiclass SVM techniques will be investigated.

History

Table of Contents

1. Introduction -- 2. Literature review -- 3. Mammographic images -- 4. Tamura's features -- 5. Feature data -- 6. Support vector machines -- 7. Results & discussion -- 8. Conclusion -- 9. Future work -- 10. Abbreviations -- Appendix -- Bibliography.

Notes

Empirical thesis. Bibliography: pages 59-60

Awarding Institution

Macquarie University

Degree Type

Thesis bachelor honours

Degree

BSc (Hons), Macquarie University, Faculty of Science and Engineering, School of Engineering

Department, Centre or School

School of Engineering

Year of Award

2016

Principal Supervisor

Yinan Kong

Rights

Copyright Aaron Mikaelian 2016. Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (xiii, 60 pages illustrations)

Former Identifiers

mq:70327 http://hdl.handle.net/1959.14/1262597

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