Texture-Based Classification of Benign and Malignant Mammography Images using Weka Machine Learning: An Optimal Approach

Abstract

: Breast cancer is the most common cancer in Indonesia. One way to detect it early is by screening using mammography. Previous trials showed that mammography screening in women aged 40-49 years could reduce breast cancer mortality by 25%. However, misdiagnosis may occur abaout breast density and the patient’s physical size due to machines. In addition, human reader errors can occur concerning the reader's experience and perception. Therefore, diagnostic aids are needed to distinguish benign and malignant cases and receive appropriate treatment. The methodology in this research consists of three stages: preprocessing, texture feature extraction, and data classification. Preprocessing consists of filtering, contrast, cropping, and resizing, while texture feature extraction consist of Histogram and (Gray Level Co-occurrence Matrix) GLCM. Data classification using Support Vector Machines (SVM), Naive Bayes, Multi-Layer Perceptron (MLP), Multiclass classifier, and Random Forest methods with Weka Machine Learning software. It produces an accuracy of 62.00%, 62.00%, 88.00%, 82.00%, and 100.00%, respectively. The results of data classification using the Random Forest method show that the accuracy, specifications, and specificity reach 100%. Random forest can be used as the most optimal classification method to distinguish benign and malignant cases based on texture features in mammography images using Weka Machine Learning software. This can help radiologists and medical professionals to diagnose cases and take further steps, such as therapy.