Disclaimer In the initial proposal of this project, the aim was to develop a Convolutional Neural Network (CNN) model capable of classifying mammogram images into three distinct categories: normal, benign, and malignant. However, during the course of the project, the… Continue Reading
Paper 9 Li, H., Chen, D., Nailon, W. H., Davies, M. E., & Laurenson, D. (2022). Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography. IEEE Transactions on Medical Imaging, 41(1), 3–13. https://doi.org/10.1109/tmi.2021.3102622 Li et al. (2022) introduce an… Continue Reading
Paper 5 Oyelade, O. N., & Ezugwu, A. E. (2022). A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-09905-3 Oyelade and Ezugwu (2022) introduce an innovative fusion of… Continue Reading
Paper 1 Tavakoli, N., Karimi, M., Norouzi, A., Karimi, N., Samavi, S., & Soroushmehr, S. M. R. (2019). Detection of abnormalities in mammograms using deep features. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-019-01639-x Tavakoli et al. (2019) presents a Convolutional… Continue Reading
Deliverables The project aims to develop a model geared towards automating mammogram reporting to improve the accuracy of breast cancer detection. As part of this endeavour, the following outputs will be generated: Deliverable/Milestone Artifact Delivery Date Assessment 1 – Project… Continue Reading