Background: Breast cancers are one of the most prevalent forms of cancers
amongst women apart from being the top second cause of cancer related deaths
across the world. A way to detect the presence of breast cancers earlier is through the
presence of minute deposits of calcium, that is, micro-calcifications in mammograms.
Detecting this much earlier is important for successfully treating the cancer.
Method: In the current work, a novel Hybrid Particle Swarm Optimized (PSO)
cascaded classifier is suggested. Wavelet Features linked to Histogram of Oriented
Gradients (HOG) based features selection technique is utilized. For the detection of
micro-calcifications utilizing the Support Vector Machines (SVM) is a supervised
learning technique which may be utilized for classifications as well as regressions.
Cascade classifications are capable of tackling all the mentioned challenges with an integrated model
through the usage of asymmetric cascades of sparse classifiers, every single one trained to attain great
detection sensitivities as well as adequate false positive rates.
Results: SVM-Poly kernels, SVM-Radial Basis Function (RBF) kernels, Cascaded classifiers, PSO
optimized cascade classifiers as well as Hybrid PSO optimized cascade classifiers are valuated for
classification accuracies, sensitivities as well as specificities. The suggested Hybrid PSO optimized
cascade classifiers outperform all other in all categories. It was observed that the proposed Hybrid PSO
optimized cascade classifier method increased classification accuracy by 2.49%-8.74%, compared with
Conclusion: Experimental evaluations proved the abilities of the suggested Hybrid PSO optimized
cascade classifiers for the exploration of features space for best features combination for malignant and
benign micro-calcifications from mammograms.