A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection

Abstract

Breast cancer is a health concern of importance, and it is crucial to detect it early for effective treatment. Recently there has been increasing interest in using artificial intelligence (AI) for breast cancer detection, which has shown results in enhancing accuracy and reducing false positives. However, there are some limitations regarding accuracy in detection. This study introduces an approach that utilizes 1D CNN as feature extraction and employs machine learning (ML) algorithms such as XGBoost, random forests (RF), decision trees (DT) support vector machines (SVM) and k nearest neighbor (KNN) to classify samples as either benign or malignant aiming to enhance accuracy. Our findings reveal that the XGBoost algorithm with feature extraction (1D CNN) achieved an accuracy of 98.24% on the test set. This study highlights the feasibility of employing machine learning algorithms and deep learning (DL). This study uses a dataset of Wisconsin breast cancer (WBC), for detecting breast cancer. The proposed approach has a good detection and improving outcomes via shows accurate and reliable tools for diagnosing breast cancer.

Keywords

• Breast cancer diagnosis, Deep learning, Machine learning, Wisconsin, 1D-CNN