Abstract

According to recent data, breast cancer is the most common cancer in the world. Every year it kills almost 900,000 individuals; precise early identification can help minimize breast cancer mortality rates. This work offers a review that illustrates the novel applications of machine learning and deep learning technologies for detecting and classifying breast cancer and provides an overview of progress in this area. It first provides an overview of the many approaches to machine learning, then an overview of the different deep learning algorithms and specialized architectures for detecting and classifying breast cancer. This paper aims to investigate the performance of various algorithms such as Support Vector Machine (SVM), Logistic Regression, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) in detecting the fatal disease. The proposed model's performance is evaluated using four metrics, i.e., accuracy, precision, recall, and F1-Score. The RNN outperformed the remaining algorithms in terms of accuracy (83%), precision (77%), and F1-Score (68%). However, ANN's recall (66%) was higher than SVM and logistic regression, CNN, RNN, and LSTM