Abubacker Kaja Mohideen

Learn More
– Breast cancer is a most common disease diagnosed in women. The Microcalcification Clusters (MCs) in the mammograms are one of the important early sign. The accurate detection of microcalcifications is a key problem in Computer Aided Detection (CAD). In this paper, we have proposed a novel association rule mining approach for classification of(More)
Detection of outliers and relevant features are the most important process before classification. In this paper, a novel semi-supervised k-means clustering is proposed for outlier detection in mammogram classification. Initially the shape features are extracted from the digital mammograms, and k-means clustering is applied to cluster the features, the(More)
Breast cancer can be diagnosed with an early training course by detecting the presence of microcalcifications in screening mammograms. The multiresolution analysis using discrete wavelet transform presents characteristics which can be exploited to develop tools for detection of microcalcifications. The objective of this work is to study the best type of(More)
Ant Colony Optimization (ACO) has been applied in wide range of applications. In ACO, for every iteration the entire problem space is considered for the solution construction using the probability of the pheromone deposits. After convergence, the global solution is made with the path which has highest pheromone deposit. In this paper, a novel solution(More)
  • 1