A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
Neural Networks, Fuzzy Logic and Genetic Algorithms
Neural Networks, Fuzzy Logic and Genetic Algorithms
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
IEEE Transactions on Information Technology in Biomedicine
Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications
IEEE Transactions on Information Technology in Biomedicine
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In this paper, a computerized scheme for automatic detection of cancerous tumors in mammograms has been examined. Diagnosis of breast tumors at the early stage is a very difficult task as the cancerous tumors are embedded in normal breast tissue structures. This paper proposes a supervised machine learning algorithm --- Modified Genetic Algorithm (MGA) tuned Artificial Neural Network for detection of tumors in mammograms. Genetic Algorithm is a population based optimization algorithm based on the principle of natural evolution. By utilizing the MGA, the parameters of the Artificial Neural Network (ANN) are optimized. To increase the detection accuracy a feature extraction methodology is used to extract the texture features of the cancerous tissues and normal tissues prior to classification. Then Modified Genetic Algorithm (MGA) tuned Artificial Neural Network classifier is applied at the end to determine whether a given input data is suspicious for tumor or not. The performance of our computerized scheme is evaluated using a database of 322 mammograms originated from MIAS databases. The result shows that the proposed algorithm has a recognition score of 97.8%.