Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Approaches for automated detection and classification of masses in mammograms
Pattern Recognition
Computers in Biology and Medicine
Diagnosis of breast cancer using Bayesian networks: A case study
Computers in Biology and Medicine
Artificial Intelligence: A Systems Approach
Artificial Intelligence: A Systems Approach
Computer-Based Identification of Breast Cancer Using Digitized Mammograms
Journal of Medical Systems
Classification of benign and malignant masses based on Zernike moments
Computers in Biology and Medicine
Ontology-based mammography annotation and Case-based Retrieval of breast masses
Expert Systems with Applications: An International Journal
Fast opposite weight learning rules with application in breast cancer diagnosis
Computers in Biology and Medicine
A multilayered ensemble architecture for the classification of masses in digital mammograms
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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The classification of benign and malignant patterns in digital mammograms is one of most important and significant processes during the diagnosis of breast cancer as it helps detecting the disease at its early stage which saves many lives. Breast abnormalities are often embedded in and camouflaged by various breast tissue structures. It is a very challenging and difficult task for radiologists to correctly classify suspicious areas (benign and malignant patterns) in digital mammograms. In the early stage, the visual clues are subtle and varied in appearance, making diagnosis difficult; challenging even for specialists. Therefore, an intelligent classifier is required which can help radiologists in classifying suspicious areas and diagnosing breast cancer. This paper investigates a novel soft clustered based direct learning classifier which creates soft clusters within a class and learns using direct calculation of weights. The feature space for suspicious areas in digital mammograms from same class patterns can have multiple clusters and the proposed classifier uses this fact and introduces a novel idea to create soft clusters for each available class and applies them to form sub-classes within benign and malignant classes. A novel learning process based on direct learning is introduced. The experiments using the proposed classifier have been conducted on a benchmark database. The results have been analysed using ANOVA test which showed that the results are statistically significant.