Machine Learning Techniques for Autonomous Lesion Detection in Microwave Breast Imaging Clinical Data
Conference paper
Shadwell, H., Nnadi, N., Aliyu, A., Ghavami, N., Ghavami, M., Tiberi, G. and Sohani, B. (2024). Machine Learning Techniques for Autonomous Lesion Detection in Microwave Breast Imaging Clinical Data. ISMICT 2024. London, UK 15 - 17 May 2024 Institute of Electrical and Electronics Engineers (IEEE).
Authors | Shadwell, H., Nnadi, N., Aliyu, A., Ghavami, N., Ghavami, M., Tiberi, G. and Sohani, B. |
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Type | Conference paper |
Abstract | Microwave breast imaging is becoming a promising new approach in the early detection of breast cancer. In this study, we have used advanced machine learning techniques to identify breast lesions from clinical data obtained via MammoWave scanner. To improve the performance of our model, we have explored refinements such as adjusting the number of principal components (PCs) in principal component analysis (PCA) and transitioning to generative adversarial network (GAN)-generated raw data. Leveraging this data, we have used PCA feature extraction, and utilized it to train a support vector machine (SVM). Our analysis has yielded promising results, with a ternary classification approach achieving an accuracy of 0.80. |
Keywords | Microwave imaging; Support vector machine (SVM); Principal component analysis (PCA); Generative adversarial network (GAN) |
Year | 2024 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Web address (URL) | https://www.ismict-2024.com/ |
Accepted author manuscript | License File Access Level Open |
Publication dates | |
15 May 2024 | |
Publication process dates | |
Accepted | 15 Apr 2024 |
Deposited | 10 Jul 2024 |
Web address (URL) of conference proceedings | https://www.ismict-2024.com/ |
https://openresearch.lsbu.ac.uk/item/979y6
Restricted files
Accepted author manuscript
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