Use of Modified Masood Score with Prediction of Dominant Features to Classify Breast Lesions
In this chapter, we propose a novel method to improve the diagnostic accuracy for breast lesions based on certain cellular and nuclear features which become the criteria for final diagnosis. In the medical field, the accuracy of the diagnosis affects proper treatment of the condition. To overcome the problem of interobserver variability, a 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 the various grades of breast lesions. We have used three classifiers: adaptive boosting, decision trees and random forests. These classifiers are effective tools for classifying breast lesions into benign, intermediate and malignant conditions. Principal component analysis using covariance and correlation is used to improve the diagnostic accuracy and for taking only the dominant features sufficient for classification.
KeywordsModified Masood score Adaptive boosting Decision tree Random forest Principal component analysis Covariance Correlation
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