Classification of Breast Lesions using Modified Masood Score and Neural Network

Sonali Nandish Manoli¹, Anand Raj Ulle¹, N. M. Nandini² and T. S. Rekha²

¹Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysuru.

²Department of Pathology, JSS Medical College, Affiliated to JSS University Mysuru.

Corresponding Author E-mail: sonalinandish@gmail.com

Abstract: In this paper, we propose a novel method to classify Breast Lesions based on minute changes in the cell and nuclear features of the cell. It is important to note these changes as they play a significant role in diagnosis and the line of treatment by an oncologist. To overcome the problem of inter-observer variability the method of scoring is used to grade the lesions considered for the study. We have used the Modified Masood Score and designed an algorithm which classifies a given breast lesion into 6 classes namely Benign, Intermediate class-1,Intermediate class-2, Malignant class-1,Malignant class-2 and Malignant class-3. We have developed a sensitive model using the feed-forward neural network and Pattern Network to achieve the above objective. The Rank of the features is observed using ReliefF Algorithm.

Keywords: Computer Aided Diagnosis; Feed-Forward Neural Network; Machine Learning, Modified Masood Score; Neural Networks; Pattern Network; Performance Function and Training Function

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