부산대학교 도서관

Breast cancer, a fatal tumor that affects both women and men, can be detected early and treated effectively to save lives. In this article, we aim to categorize breast cancer data as either benign or malignant tumors using various techniques such as Deep Learning and Machine Learning. We employ Sequential Neural Networks, Logistic Regression, Random Forest, Decision Tree, and SVM for classification purposes. Upon comparing the results of each classifier, we determine that the Sequential Neural Network achieves the highest accuracy score of 98% in distinguishing between benign and malignant cancers. Accurate classification enables prompt detection of the disease, benefiting patients and doctors alike in their efforts to combat breast cancer.