Leveraging SLIC Superpixel Segmentation and Cascaded Ensemble SVM for Fully Automated Mass Detection In Mammograms
This addresses the challenge of detecting breast masses in mammograms, which is crucial for medical diagnosis, but it appears incremental as it builds on existing segmentation and SVM techniques.
The paper tackles the problem of automated mass detection in mammograms by proposing a segmentation method with a cascaded ensemble SVM, achieving a True Positive Rate of 0.82 with only 1.0 False Positives per Image.
Identification and segmentation of breast masses in mammograms face complex challenges, owing to the highly variable nature of malignant densities with regards to their shape, contours, texture and orientation. Additionally, classifiers typically suffer from high class imbalance in region candidates, where normal tissue regions vastly outnumber malignant masses. This paper proposes a rigorous segmentation method, supported by morphological enhancement using grayscale linear filters. A novel cascaded ensemble of support vector machines (SVM) is used to effectively tackle the class imbalance and provide significant predictions. For True Positive Rate (TPR) of 0.35, 0.69 and 0.82, the system generates only 0.1, 0.5 and 1.0 False Positives/Image (FPI), respectively.