Malignant and benign lesion classification is a challenging process that requires the best possible fusion of several imaging parameters in relation to tissue density heterogeneity, prediction of lesion boundaries, and change of surrounding tissues. Recent research has shown that important picture properties such as intensity, energy, homogeneity, entropy, and statistical moments, among others, may be modelled using statistics and texture features. As a result, this method was created to make early predictions about the identification of breast lesions in digital image processing. Preprocessing, features extraction, and classification are among the phases that make up the project. The main goal of the suggested technique is to categorise lesions into benign and malignant categories. Additionally to enhance feature categorization

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