DISCUSSION In evaluating the performance of our Convolutional Neural Network (CNN) model, two critical factors emerge as potential contributors to its suboptimal results: data imbalance and model architecture. Before delving into these specific aspects, it is essential to understand their… Continue Reading
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
Embarking on a preliminary literature review is crucial for gaining comprehensive insights into methodologies and challenges associated with developing Convolutional Neural Network (CNN) frameworks for breast cancer detection. This exploration serves as a foundational knowledge base, providing a nuanced understanding… Continue Reading
Figure 1 Process for CNN Model Construction Data Collection This project will leverage a total of 2005 high-resolution open-source mammograms, comprising 1003 contrast-enhanced spectral mammograms (CESM) and 1002 subtracted mammograms from Khaled et al.’s dataset (2021). Within the 1003 CESM,… Continue Reading
Background Breast cancer stands as a prominent global health concern, predominantly impacting women, with 2.3 million new diagnoses and 685,000 deaths reported globally in 2020, according to 2023 World Health Organisation statistics. Despite its prevalence, the disease boasts a curability… Continue Reading
Image 1: The procedure of mammography examination and anatomy of breast. Mammography examination is a low cost non-invasive x-ray examination that is specialised for breast only. According Ren et al. (2022), it is recommended that women who is age 40… Continue Reading