Classification of Invasive Ductal Carcinoma and Invasive Lobular Carcinoma of Breast Cancer Using the Artificial Neural Network Recurrent Algorithm

Abstract

Aims: The purpose of this study is to classify invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) of breast cancer using the artificial neural networks recurrent algorithm. the use of artificial neural networks Recurrent algorithms can improve the accuracy of breast cancer diagnosis and lead to more effective treatment plans. Study Design: The method employed is a cross-sectional design. Place and Duration of Study: The research was conducted in the Computer Laboratory Department of Informatics, Faculty of Mathematics and Natural Sciences, Udayana University, Bali Indonesia. Methodology: Utilizing physical parameters from mammographic images as input variables for the artificial neural network algorithm. Results: For Invasive Ductal Carcinoma, the accuracy is 77.5%, sensitivity (recall) is 55%, precision is 100%, F1-Score is 60.97%, specificity is 100%, FPR is 0, and TPR is 0.55. For Invasive Lobular Carcinoma, the accuracy is 77.5%, sensitivity (recall) is 100%, precision is 68.97%, F1-Score is 81.63%, specificity is 55%, FPR is 0.45, and TPR is 1. Conclusion: The artificial neural network algorithm is capable of classifying Invasive Ductal Carcinoma and Invasive Lobular Carcinoma effectively.