Classification of Breast Lesions using Modified Masood Score and Neural Network
Sonali Nandish Manoli1, Anand Raj Ulle1, N. M. Nandiniand 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.

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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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