Classification of benign and malignant masses based on Zernike moments
Computers in Biology and Medicine
Fast opposite weight learning rules with application in breast cancer diagnosis
Computers in Biology and Medicine
| Hi-index | 0.00 |
The cancer treatment is effective only if it is detected at an early stage. In this context, Mammography is the most efficient method for early detection. Due to the complexity of this last, the distinction of microcalcifications or opacities is very difficult. This paper deals with the problem of shape feature extraction in digital mammograms, particularly the boundary information. In fact, we evaluated the efficiency on boundary information possessed by mass region. We propose feature vector based in boundary analysis in ameliorating three points of view like RDM, convexity and angular features. We use the Digital Database for Screening Mammography “DDSM” for experiments. Some classifiers as Multilayer Perception “MLP” and k-Nearest Neighbours “kNN” are used to distinguish the pathological records from the healthy ones. Using “MLP” classifiers we obtained 94,2% as sensitivity (percentage of pathological ROIs correctly classified). The results in term of specificity (percentage of non-pathological ROIs correctly classified) grows around 97,9% using MLP classifier.