2020/10/20 by Jaime Simarro, Simarro, Jaime, Zohaib Salahuddin +5
Computer Science · Neuroscience · #AI in cancer detection #Artificial intelligence #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #False positive paradox #Grayscale #Image (mathematics) #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Pattern recognition (psychology) #Segmentation #Support vector machine #True positive rate #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2010.10340
arxiv created 2020/10/20 · openalex publication_date 2020/10/20 · arxiv updated 2020/10/21 · openalex created_date 2020/10/29 · openalex updated_date 2026/07/28
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.