Breast Cancer Detection with Computer Cision and Image Processing
The analysis of mammographic images is a critical task that relies on experienced radiologists to identify subtle signs of malignancy, often hidden within complex tissue patterns. However, the high volume of images and the variability caused by radiologist fatigue pose challenges to maintaining accuracy and consistency. To address these issues, Machine Learning (ML) object detection models, combined with advanced image processing techniques like CLAHE, have been introduced to enhance visualization and automate abnormality detection with precision. This integration, supported by the collaborative efforts of UoM and UDG, has significantly optimized diagnostic workflows, reducing analysis time and improving early detection, marking a transformative step in modern healthcare diagnostics.
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