Speaker
Description
Breast cancer is one of the most common diseases affecting women around the world, with millions of new cases each year and a high rate of mortality. Although advances in detection and treatment have led to better outcomes for many, some regions still struggle with limited access to early diagnosis. Detecting breast cancer earlier is essential because it greatly increases the chances of successful treatment, highlighting the importance of having accessible and accurate diagnostic tools available.
This study focuses on improving the segmentation and classification of breast tumors in mammographic images by examining the impact of various preprocessing techniques on the performance of advanced object detection models. We use the Digital Database for Screening Mammography (DDSM), a benchmark dataset for mammography analysis, to evaluate three state-of-the-art object detection models: Faster R-CNN, YOLOv8, and YOLOv9. These models are trained on images processed with different methods to observe how each technique affects the accuracy of tumor segmentation and classification into benign or malignant categories.
The primary contribution of this study is the comparative analysis of preprocessing effects on AI model performance in tumor detection, aiming to identify an optimal preprocessing approach that maximizes accuracy and reduces misclassification rates. In addition to processing raw mammograms, we evaluate the average accuracy improvements each preprocessing method offers, analyzing each model’s response to enhanced image clarity and feature visibility.
