A Convolutional Neural Networks Model for Breast Tissue Classification

Abstract

The diagnosis of breast cancer and the determination of the cancer types are essential pieces of information for cancer research in monitoring and managing the disease. In recent years, artificial intelligence techniques have led to many developments in medicine, as any information about the patient has become more valuable. In particular, artificial intelligence methods used in the detection and classification of cancer tissues directly assist physicians and contribute to the management of the treatment process. This study aims to classify breast tissues with ten different tissue characteristics by utilizing the breast tissue data set, which has 106 electrical impedance spectroscopies taken from 64 patients in the UCI Machine Learning Repository database. Various machine learning algorithms including k-nearest neighbors, support vector machine, decision tree, self-organizing fuzzy logic, and convolutional neural networks are used to classify these tissues with an accuracy of 81%, 78%, 82%, 92%, and 96%, respectively. This study demonstrated the benefit of the usage of convolutional neural networks in cancer detection and tissue classification. Compared to traditional methods, convolutional neural networks provided better and more reliable results.